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What Is Image Annotation and How Is It Used To Build AI Models?
By . February 11, 2021
How Companies Use Image Annotation to Produce High-Quality Training Data
Applications of Image Annotation
Agriculture, manufacturing, types of image annotation.
- 2D Bounding Boxes: Annotators apply rectangles and squares to define the location of the target objects. This is one of the most popular techniques in the image annotation field.
- Cuboids, or 3D Bounding Boxes: Annotators apply cubes to the target object to define the location and the depth of the object.
- Polygonal Segmentation: When target objects are asymmetrical and don’t easily fit into a box, annotators use complex polygons to define their location.
- Lines and Splines: Annotators identify key boundary lines and curves in an image to separate regions. For example, annotators may label the various lanes of a highway for a self-driving car image annotation project.
How to make image annotation easier, insight from appen image annotation expert, liz otto hamel.
- Define the scope. Begin with a clear and narrow definition of the business goals of your project. Requirements of your labeled data including annotation geometries, metadata, ontologies, and formats will stem from the business goals of the project. Using the business value to guide your image annotation project will keep things on a clear path.
- Plan to iterate. Define an initial set of requirements for your labeled data and then run a pilot. Label a small subset of the data yourself. In iterating, you will discover edge cases that may need to be accounted for in the project requirements. It can help to work with a data labeling partner that offers tooling and expertise that covers a wide variety of annotation use cases and can adapt to fit your needs.
- Plan to integrate. To combat data drift—changes in the types of data your model sees in the wild—you will want to build a scalable, automated training data pipeline in order to continuously train your model with new data. It can help to work with a data labeling partner that can scale rapidly as the volume of training data you need increases. The bigger the audience interacting with your model, the faster the amount of image annotation needed to keep the model fresh will also grow. It’s critical to plan for this from the outset.
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Image Annotation: Definition, Use Cases & Types 
Garbage in, garbage out.
This concept rules the computer science world, and for a reason.
The quality of your input data determines the quality of the output. And if you are trying to build reliable computer vision models to detect, recognize, and classify objects, the data you use to feed the learning algorithms must be accurately labeled.
And here comes the bad news—
Image annotation is a whole lot more nuanced than most people realize. And annotating your image data incorrectly can be expensive.
Luckily, you've come to the right place.
In the next few minutes, we'll explain to you the ins and outs of image annotation and walk you through some of the best practices to ensure that your data is properly labeled.
Here’s what we’ll cover:
What is image annotation?
How does image annotation work, tasks that need annotated data, types of image annotation shapes, how to find quality image data, image annotation using v7.
Solve any video or image labeling task 10x faster and with 10x less manual work.
Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.
And hey—in case you want to skip the tutorial and start annotating your data right away, check out:
- V7 Image Annotation
- V7 Video Annotation
- V7 Model Training
- V7 Auto Annotation
Image annotation is the process of labeling images in a given dataset to train machine learning models.
When the manual annotation is completed , labeled images are processed by a machine learning or deep learning model to replicate the annotations without human supervision.
Image annotation sets the standards, which the model tries to copy, so any error in the labels is replicated too. Therefore, precise image annotation lays the foundation for neural networks to be trained, making annotation one of the most important tasks in computer vision.
The process of a model labeling images on its own is often referred to as model-assisted labeling.
Image annotations can be performed both manually and by using an automated annotation tool.
Auto annotation tools are generally pre-trained algorithms that can annotate images with a certain degree of accuracy. Their annotations are essential for complicated annotation tasks like creating segment masks, which are time-consuming to create.
In these cases, auto-annotate tools assist manual annotation by providing a starting point from which further annotation can proceed.
Manual annotation is also generally assisted by tools that help record key points for easy data labeling and storage of data.
💡 Pro tip: Looking for other options? Check out 13 Best Image Annotation Tools.
Why does ai need annotated data.
Image annotation creates the training data that supervised AI models can learn from.
The way we annotate images indicates the way the AI will perform after seeing and learning from them. As a result, poor annotation is often reflected in training and results in models providing poor predictions.
Annotated data is specifically needed if we are solving a unique problem and AI is used in a relatively new domain. For common tasks like image classification and segmentation, there are pre-trained models often available and these can be adapted to specific use cases with the help of Transfer Learning with minimal data.
Training a complete model from scratch, however, often requires a huge amount of annotated data split into the train, validation, and test sets , which is difficult and time-consuming to create.
Unsupervised algorithms on the other hand do not require annotated data and can be trained directly on the raw collected data.
Now, let's get into the nitty-gritty of how image annotation actually works.
There are two things that you need to start labeling your images: an image annotation tool and enough quality training data. Amongst the plethora of image annotation tools out there, we need to ask the right questions for finding out the tool that fits our use case.
Choosing the right annotation tool requires a deep understanding of the type of data that is being annotated and the task at hand.
You need to pay particular attention to:
- The modality of the data
- The type of annotation required
- The format in which annotations are to be stored
Given the huge variety in image annotation tasks and storage formats, there are various tools that can be used for annotations. From open-source platforms, such as CVAT and LabelImg for simple annotations to more sophisticated tools like V7 for annotating large-scale data.
Furthermore, annotation can be done on an individual or organizational level or can be outsourced to freelancers or organizations offering annotation services.
Here's a quick tutorial on how to start annotating images.
1. Source your raw image or video data
The first step towards image annotation requires the preparation of raw data in the form of images or videos.
Data is generally cleaned and processed where low quality and duplicated content is removed before being sent in for annotation. You can collect and process your own data or go for publicly available datasets which are almost always available with a certain form of annotation.
💡 Pro tip: You can find quality data for your computer vision projects here: 65+ Best Free Datasets for Machine Learning.
2. find out what label types you should use.
Figuring out what type of annotation to use is directly related to what kind of task the algorithm is being taught. In case the algorithm is learning image classification, labels are in the form of class numbers. If the algorithm is learning image segmentation or object detection, on the other hand, the annotation would be semantic masks and boundary box coordinates respectively.
3. Create a class for each object you want to label
Most supervised Deep Learning algorithms must run on data that has a fixed number of classes. Thus, setting up a fixed number of labels and their names earlier can help in preventing duplicate classes or similar objects labeled under different class names.
V7 allows us to annotate based on a predefined set of classes that have their own color encoding. This makes annotation easier and reduces mistakes in the form of typos or class name ambiguities.
4. Annotate with the right tools
After the class labels have been determined, you can proceed with annotating your image data.
The corresponding object region can be annotated or image tags can be added depending on the computer vision task the annotation is being done for. Following the demarcation step, you should provide class labels for each of these regions of interest. Make sure that complex annotations like bounding boxes, segment maps, and polygons are as tight as possible.
5. Version your dataset and export it
Data can be exported in various formats depending upon the way it is to be used. Popular export methods include JSON, XML, and pickle.
For training deep learning algorithms, however, there are other formats of export like COCO, Pascal VOC which came into use through deep learning algorithms designed to fit them. Exporting a dataset in the COCO format can help us to plug it directly into a model that accepts that format without the additional hassle of accommodating the dataset to the model inputs.
V7 supports all of these export methods and additionally allows us to train a neural network on the dataset we create.
💡 Pro tip: Check out V7 Dataset Management.
How long does image annotation take.
Annotation times are largely dependent on the amount of data required and the complexity of the corresponding annotation. Simple annotations which have a limited number of objects to work on are faster than annotations containing objects from thousands of classes.
Similarly, annotations that require the image to be tagged are much faster to complete than annotations involving multiple keypoints and objects to be pinpointed.
Now, let's have a look at the list of computer vision tasks that require annotated image data.
Image classification refers to the task of assigning a label or tag to an image. Typically supervised deep learning algorithms are used for Image Classification tasks and are trained on images annotated with a label chosen from a fixed set of predefined labels.
Annotations required for image classification come in the form of simple text labels, class numbers, or one-hot encodings where a zero list containing all possible unique IDs is formed—and a particular element from the list based on the class label is set to one.
Often other forms of annotations are converted into one-hot form or class ID form before the labels are used in corresponding loss functions.
Object detection & recognition
Object detection (sometimes referred to as object recognition ) is the task of detecting objects from an image.
The annotations for these tasks are in the form of bounding boxes and class names where the extreme coordinates of the bounding boxes and the class ID are set as the ground truth.
This detection comes in the form of bounding boxes where the network detects the bounding box coordinates of each object and its corresponding class labels.
Image segmentation refers to the task of segmenting regions in the image as belonging to a particular class or label.
This can be thought of as an advanced form of object detection where instead of approximating the outline of an object in a bounding box, we are required to specify the exact object boundary and surface.
Image segmentation annotations come in the form of segment masks, or binary masks of the same shape as the image where the object segments from the image mapped onto the binary mask are marked by the corresponding class ID, and the rest of the region is marked as zero. Annotations for image segmentation often require the highest precision for algorithms to work well.
Semantic Segmentation is a specific form of image segmentation where the algorithm tries to divide the image into pixel regions based on categories.
For example, an algorithm performing semantic segmentation would group together a group of people under a common category person, creating a single mask for each category. Since the differentiation between different instances or objects of the same category is not done, this form of segmentation is often known as the simplest segmentation task.
Instance segmentation refers to the form of segmentation where the task is to separate and segment object instances from the image. Instead of singling out the categories from the image, instance segmentation algorithms work to identify and separate similar objects from groups.
Panoptic segmentation can be referred to as the conjunction of both semantic and instance segmentation where the algorithm has to segment out both object categories while paying attention to instance level segments.
This ensures that each category, as well as the object instance, gets a segment map for itself. Needless to say, this segmentation task is often the hardest amongst the three as the amount of information to be regressed by the network is quite large.
Different tasks require data to be annotated in different forms so that the processed data can be used directly for training.
While simple tasks like classification require the data to be annotated only with simple tags, complex tasks like segmentation and object detection require the data to have pixel map annotations and bounding box annotations respectively.
We've listed below a compilation of the different forms of annotation used for these tasks.
Bounding box annotations , as the name suggests, are annotations that require specific objects in an image to be covered by a bounding box. These annotations are generally needed for object detection algorithms where the box denotes the object boundaries.
They are generally not as precise as segmentation or polygonal annotations but meet the precision needed in detector use cases. These annotations are often used for training algorithms for self-driving cars and in intelligent video analytics mechanisms.
Polygon masks are generally more precise as compared to bounding boxes. Similar to bounding boxes, polygon masks try to cover an object in an image with the help of a polygon.
The increased precision comes from the increased corners that a polygon can have as compared to the restricted four vertex mask in bounding boxes. Polygonal masks do not occupy much space and can be vectorized easily, thus creating a balance between space and accuracy.
These masks are used to train object detection and semantic segmentation algorithms. Polygon masks find their use in annotations for medical imaging data and n natural data involving scene text for scene text recognition and localization.
Cuboidal annotations are an extension of object detection masks in the three-dimensional plane. These annotations are essential when detection tasks are performed on 3-dimensional data, generally observable in medical domains in the form of scans.
These annotations might also find use in training algorithms for the motion of robots and cars and in the usage of robotic arms in a three-dimensional environment.
Semantic annotations form one of the most precise forms of annotation, where the annotation comes in the form of a segmented mask of the same dimension as the input, with pixel values concerning the objects in the input.
These masks find wide-scale applicability in various forms of segmentation and can also be extended to train object detection algorithms. Semantic masks come in both two-dimensional and three-dimensional forms and are developed in correspondence with the algorithm they are required for.
Semantic segmentation finds a wide range of use in computer vision for self-driving cars and medical imaging . In medical imaging, segmentation helps in the identification and localization of cells, enabling the formulation of an understanding of their shape features like circularity, area, and size.
💡 Pro tip: Check out 7 Life-Saving AI Use Cases in Healthcare.
In self-driving cars, segmentation helps to single out pedestrians and obstacles in the road, reducing road accidents considerably.
V7 allows us to perform fast and easy segmentation annotation with the help of the auto-annotate tool.
While the creation of segment masks requires a huge amount of time, auto annotate works by creating a segmented mask automatically on a specified region of interest.
Polyline annotations come in the form of a set of lines drawn across the input image called polylines. These Polylines are used to annotate boundaries of objects and find use cases primarily in tasks like lane detection which require the algorithm to predict lines as compared to classes.
High precision polyline annotations can help train algorithms for self-driving cars to choose lanes accurately and ascertain “drivable regions” to safely navigate through roads.
Keypoint or landmark annotations come in the form of coordinates that pinpoint the location of a particular feature or object in an image. Landmark annotations are mainly used to train algorithms that scrutinize facial data to find features like eyes, nose, and lips, and correlate them to predict human posture and activity.
Apart from finding use in facial datasets, landmarks are also used in gesture recognition, human pose recognition , and counting objects of a similar nature in an image. V7 allows you to pre-shape skeleton backbones that can be used to construct landmarks in no time by overlaying the corresponding shape on an image.
For more information, check out the tutorial on skeletal annotations here:
High-quality annotated data is not easy to obtain.
If data of a particular nature is not available in the public domain, annotations have to be constructed from raw collected data. This often includes a multitude of tests to ensure that the processed data is free from noise and is completely accurate.
Here are a few ways to obtain quality image data.
Open datasets are the easiest source of high-quality annotated data. Large-scale datasets like Places365, ImageNet, and COCO are released as a byproduct of research and are maintained by the authors of the corresponding articles. These datasets, however, are used mainly by academic researchers working on a better algorithm as commercial usage is typically restricted.
💡 Pro tip: Check out 20+ Open Source Computer Vision Datasets.
Self annotated data.
As an alternative to open datasets, you can collect and annotate raw data.
While raw data can be in the form of captured images with the help of a camera, it can also be obtained from open source webpages like CreativeCommons, Wikimedia, and Unsplash. Open source images form an excellent source of raw data and reduce the workload of dataset creation immensely.
Captured image data can also be in the form of medical scans, satellite imagery, or drone photographs.
💡 Pro tip: Read Data Annotation Tutorial: Definition, Tools, Datasets.
Scrape web data.
Web scraping refers to scourging the internet for obtaining images of a particular nature with the help of a script that runs searches repeatedly and saves the relevant images.
While scraping web data is an easy and fast method of obtaining data, this data is almost always in a very raw form and has to be cleaned thoroughly before any algorithm or annotation can be performed. Since scraping can help us gather images based on the query we set it up with, the images are already known to belong to a certain class or topic.
This makes annotation much easier, particularly for tasks like classification which require only a single tag for each image.
Pixel-perfect annotation quality, increased efficiency, and lowered overheads.
V7 provides a lot of tools that are useful for image annotation. All of the tools discussed in this post are covered by V7 as part of their data annotation services.
Here’s a quick guide on getting started with image annotation using V7.
1. Collect, prepare, and upload your image data
Upload your data using the data upload feature on the webpage or use the command-line interface (CLI) facilitated by Darwin.
V7 offers advanced dataset management features that allow you to easily organize and manage your data from one place.
2. Choose the annotation type/class label
Choose the annotation type for a specific class from the list of available annotations. You can change this or add new classes anytime by going to the “Classes” tab located on the left side of the interface. You can also add a short description of the annotation type and class to help other annotators understand your work.
3. Start annotating
Finally, start annotating your data either manually or using V7's auto-annotation tool.
V7 offers a real-time collaborative experience so that you can get your whole team on the same page and speed up your annotation process. To describe your image in greater detail, you can add sub annotations such as:
- Instance ID
- Direction Vector
You can also add comments and tag your fellow teammates. And hey—don’t forget the hotkeys.
V7 comes equipped with several built-in power user shortcuts to speed up your annotation process and avoid fatigue.
Apart from that, you can also perform OCR by using V7's built-in public Text Scanner model.
Feel free to get in touch with our team to discuss your project.
Data collection and annotation is one of the most cumbersome parts of working with data.
Yet it forms the baseline for training algorithms and must be performed with the highest precision possible. Proper annotation often saves a lot of time in the later stages of the pipeline when the model is being developed.
Curious to learn more? Check out:
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Hmrishav Bandyopadhyay studies Electronics and Telecommunication Engineering at Jadavpur University. He previously worked as a researcher at the University of California, Irvine, and Carnegie Mellon Univeristy. His deep learning research revolves around unsupervised image de-warping and segmentation.
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Home » Image Annotation for Computer Vision: A Practical Guide
Image Annotation for Computer Vision: A Practical Guide
In This Article
What Is Image Annotation?
Image annotation is the practice of assigning labels to an image or set of images. A human operator reviews a set of images, identifies relevant objects in each image, and annotates the image by indicating, for example, the shape and label of each object. These annotations can be used to create a training dataset for computer vision models. The model uses human annotations as its ground truth, and uses them to learn to detect objects or label images on its own. This process can be used to train models for tasks like image classification, object recognition, and image segmentation.
The number of labels assigned to an image can vary depending on the type and scope of the project. In some cases, a single label is sufficient to represent an entire image. In other cases, annotators identify specific objects, segment an image into relevant regions, or identify landmarks, which are specific points of interest in an image. To ensure labeling accuracy, it is common to allow multiple annotators to label the same image, with majority voting to select the label that is most likely to be correct.
This is part of an extensive series of guides about machine learning .
How Does Image Annotation Work?
Image annotation projects involve large scale annotation of images by teams of human annotators. The annotators must be well trained in the requirements of the project and adept at accurately performing the necessary annotations.
Image annotation work typically includes the following tasks:
- Preparing the image dataset
- Specifying object classes that annotators will use to label images
- Assigning labels to images
- Marking objects within each image by drawing bounding boxes
- Selecting object class labels for each box
- Exporting the annotations in a format that can be used as a training dataset
- Post processing of the data to check if labeling is accurate
- In case of inconsistent labeling, the system should enable a second or third labeling round with voting between annotators
What Are Image Annotation Tools?
There are several open source and freeware tools available for annotating images. A common open source tool used in many large-scale projects is Computer Vision Annotation Tool (CVAT). Image annotation tools support the annotation process itself (for example, they enable drawing complex shapes on an image), and provide a structured labeling system so annotators can apply the correct labels to image artifacts.
Key features and capabilities of image annotation platforms include:
- Efficient user interface— should support fast labeling, reduce human error, and be intuitive for the workforce without extensive training.
- Ontology and taxonomy support —should be configurable to the label structure required for the machine learning model, including classifications, hierarchical relationships and custom variables.
- A nnotation quality —accuracy in image annotation is paramount. An annotation platform should support multiple techniques for measuring quality including benchmarking (comparing annotation work to a gold standard) and consensus (comparing labels from two or more annotators who work on the same task).
- User management —should support remote user management, the ability to define supervisors who can review work by annotators, and collaboration features to enable supervisors to provide feedback on the work.
- Automation —advanced annotation platforms can reduce human error and make annotation work more efficient by automating complex annotation tasks. Automated pixel maps and label suggestions can be a starting point for human annotators.
- Supports common formats —the tool should export annotation data in a simple format that users can easily understand and use in machine learning models.
Types of Image Annotation
Image annotation involves assigning labels to help artificial intelligence (AI) models detect certain aspects within a visual representation. Different types of image annotation help represent different aspects of an image.
These concepts are used in many types of image annotation:
- Lines – lines can help annotate objects in an image to enable machines to identify boundaries.
- Polygons – polygons help annotate objects that are neither symmetric nor regular. It involves placing dots across the dimensions of an object and manually drawing lines along the object’s perimeter or circumference.
- Markers – some annotations involve placing markers on coordinates in the image that have special significance.
Image classification enables machines to objects in images and across an entire dataset. The classifier is trained on a labeled dataset, learning to classify new unseen images into the same set of labels.
The process of preparing images for image classification is commonly known as annotation or tagging. This involves adding tags that describe objects or scenes in the image – for example, you can tag exterior images of a building with labels like “fence” or “garden” and interior images of the building as “elevator” or “stairs”.
Object Recognition and Detection
Object recognition, or object detection, enables machines to:
- Identify a particular object in an image and apply the accurate label.
- Identify the presence of multiple objects, including the number of instances and locations, and apply the accurate labels.
You can repeat this process using different image sets to train a machine learning model to autonomously identify and label these objects in new images. Object recognition-compatible techniques like polygons or bounding boxes can help you label different objects in a single image. For example, you can annotate cars, bikes, and pedestrians separately in one image.
Landmarking enables machine learning models to identify facial features, expressions, emotions, and gestures. This technique can also serve to mark the position and orientation of a human body.
For example, you can use data labels to mark specific locations on the face, like lips, eyebrows, eyes, and forehead, with specific numbers. Your machine learning model uses these marks to learn the different parts of a human face.
- Image Segmentation
Image segmentation enables machines to locate boundaries and objects in an image. This technique achieves higher accuracy for classification tasks. Image segmentation involves dividing an image into several segments, assigning every pixel to specific classes or class instances.
Here are the three classes of image segmentation:
- Semantic segmentation —helps identify the boundaries between similar objects.
- Instance segmentation —helps identify and label each object in an image.
- Panoptic segmentation —uses semantic segmentation to produce data labeled for background and instance segmentation to label the objects in the image.
Boundary recognition enables machines to identify the boundaries or lines of objects in an image. These boundaries can include:
- Regions of topography present in an image
- The edges of a specific object
An annotated image can help train your models to identify similar patterns in unlabeled images. Boundary recognition is particularly helpful in enabling self-driving cars to operate safely.
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Challenges in the image annotation process for computer vision.
Here are notable challenges in the image annotation process:
Balancing costs with accuracy levels
There are two primary data annotation methods—human annotation and automated annotation. Human annotation typically takes longer and costs more than automated annotation, and also requires training for annotators, but achieves more accurate results. In comparison, automated annotation is more cost-effective but it can be difficult to determine the accuracy level of the results.
Guaranteeing consistent data
Machine learning models need a good quality of consistent data to make accurate predictions. However, data labelers may interpret subjective data differently due to their beliefs, culture, and personal biases. If data is labeled inconsistently, the results of a machine learning model will also be skewed.
Choosing a suitable annotation tool
There are many image annotation platforms and tools, each providing different capabilities for different types of annotations. The variety of offerings can make it difficult to choose the most suitable tools for each project. It can also be challenging to choose the right tool to match the skillsets of your workforce.
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Image Annotation in 2023: Definition, Importance & Techniques
What is image annotation?
Why is image annotation important now, what are the techniques for image annotation, how to annotate images and videos, in-housing vs outsourcing vs crowdsourcing.
Image annotation is one of the most important stages in the development of computer vision and image recognition applications, which involves recognizing, obtaining, describing, and interpreting results from digital images or videos. Computer vision is widely used in AI applications such as autonomous vehicles, medical imaging, or security. Therefore, image annotation plays a crucial role in AI/ML development in many sectors.
Supervised ML models require data labeling to work effectively. Image annotation is a subset of data annotation where the labeling process focuses only on visual digital data such as images and videos.
Image annotation often requires manual work. An engineer determines the labels or “tags” and passes the image-specific information to the computer vision model being trained. You can think of this process like the questions a child asks her parents to explore the environment in which she lives. The parents categorize the data into universal phrases such as bananas, oranges, cats, etc., as shown in the below image.
Computer vision has already changed our lives with applications in healthcare, automotive, or marketing . According to Forbes , the computer vision market value will be around $50 billion in 2022 and PWC predicts that driverless cars could account for 40% of miles driven by 2030.
Clickworker offers image annotation and data collection services through a crowdsourcing platform. Their global team of over 4 million registered workers provides:
- AI/ML training datasets
- Image annotation services
- And other data-related services
There are five main techniques of image annotation, namely:
A frame is drawn around the object to be identified. Bounding boxes can be used for both two- and three-dimensional images.
Landmarking is an effective technique for identifying facial features, gestures, facial expressions and emotions. It is also used to mark body position and orientation. As shown in the figure below, data labelers mark specific locations on the face, such as eyes, eyebrows, lips, forehead, and so on with specific numbers by using this information ML model learns the parts of the human face.
These are pixel-level annotations that hide some areas of an image and make other areas of interest more visible. You can think of this technique as an image filter that makes it easier to focus on certain areas of the image.
This technique is used to mark the pick point of the target object and frame its edges: The polygon technique is a useful tool for labeling objects with irregular shapes.
The polyline technique helps create ML models for computer vision that guide autonomous vehicles. It ensures ML models recognize objects on the road, directions, turns, and oncoming traffic to perceive the environment for safe driving.
Your company needs an image annotation tool to label the visual data. There are vendors that offer such tools for a fee. There are also open source image labeling tools that you can use freely. Moreover, they are modifiable, which means you can change them according to your business needs.
Developing your own tool for image annotation could be an alternative to outsourcing software. However, like all in-house activities, this is a more time-consuming and capital-intensive approach. However, if you have sufficient resources and feel that the templates available on the market do not meet your requirements, developing your own tool is possible.
To learn more, check out our comprehensive articles on video annotation and video annotation tools
Image annotation techniques require some manual work. Deciding who should perform this manual task is an important strategic decision for organizations. It is because the main methods, namely in-house, outsourcing and crowdsourcing, offer different levels of cost, output quality, data security, etc.
It is important to note that there is no prescribed strategy for choosing between these methods. The optimal strategy will vary depending on the conditions and needs of your organization. Nevertheless, the following table might be helpful for you to select the optimal strategy. For more information, you can click here .
You can check our sortable/filterable list of data labeling/annotation/classification vendors list.
To learn more about annotation you can also read our text and audio annotation articles. If you also need help choosing vendors for image annotation, we can help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE and NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and resources that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch like Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Clarifying Image Recognition Vs. Classification in 2023
Guide to object detection & its applications in 2023, 5 crowdsourcing image annotation benefits in 2023.
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Image Annotation: Best Software Tools and Solutions in 2023
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Image annotation plays a significant role in computer vision, the technology that allows computers to gain a high-level understanding from digital images or videos. Annotation, or image tagging, is a primary step in the creation of image recognition algorithms and deep learning models.
The software platforms used for image annotation have greatly advanced over the past years. Key industry trends include data security and privacy. There is a growing need to standardize and integrate how companies acquire training data, annotate it, train models, and use them in applications.
In particular, this article will discuss:
- Introduction: What is image annotation, and why is it needed?
- Process of annotating images: Successfully annotate image datasets
- Annotation solutions: Best software platforms for image annotation
About us: We provide the Viso Suite , the world’s only end-to-end computer vision platform. One solution with revolutionary infrastructure for image annotation, data collection, datasets, model training, application development, and deployment. Get a demo for your team.
What is Image Annotation?
Image annotation is the process of labeling images of a dataset to train a machine learning model. Therefore, image annotation is used to label the features you need your system to recognize. Training an ML model with labeled data is called supervised learning (see supervised vs. unsupervised learning ).
The annotation task usually involves manual work, sometimes with computer-assisted help. A Machine Learning engineer predetermines the labels, known as “classes”, and provides the image-specific information to the computer vision model. After the model is trained and deployed, it will predict and recognize those predetermined features in new images that have not been annotated yet.
Popular annotated image datasets are the Microsoft COCO Dataset (Common Objects in Context), with 2.5 million labeled instances in 328k images, and Google’s OID (Open Images Database) dataset with approximately 9 million pre-annotated images.
Why is Image Annotation needed?
Labeling images is necessary for functional datasets because it lets the training model know what the important parts of the image are (classes) so that it can later use those notes to identify those classes in new, never-before-seen images.
Video annotation is based on the concept of image annotation. For video annotation, features are manually labeled on every video frame (image) to train a machine learning model for video detection. Hence, the dataset for a video detection model is comprised of images for the individual video frames.
The video below shows video-based real-time object detection and tracking with deep learning. The application was built on the Computer Vision Platform Viso Suite .
When do I need to annotate images for Computer Vision?
To train and develop computer vision algorithms based on deep neural networks (DNN), data annotation is needed in cases where pre-trained models are not specific or accurate enough.
As mentioned before, there are enormous public image datasets available, with millions of image annotations ( COCO , OID, etc.). For common and standardized object detection problems (e.g. person detection), an algorithm that is trained on a massive public dataset ( pre-trained algorithm) provides very good results and the benefits of additional labeling do not justify the high additional costs in those situations.
However, in some situations, image annotation is essential:
- New Tasks: Hence, image annotation is important when AI is applied to new AI tasks without appropriate annotated data available. For example, in industrial automation, computer vision is frequently applied to detect specific items and their condition.
- Restricted Data: While there is plenty of data available on the internet, some image data requires a license agreement and its use may be restricted for the development of commercial computer vision products. In some areas such as medical imaging, manual data annotation generally comes with privacy concerns, when sensitive visuals (faces, identifiable attributes, etc.) are involved. Another challenge is the use of images that contains a companies’ intellectual property.
How does Image Annotation work?
To annotate images, you can use any open source or freeware data annotation tool. The Computer Vision Annotation Tool (CVAT) is probably the most popular open-source image annotation tool.
While dealing with a large amount of data, a trained workforce will be required to annotate the images. Companies use their own data scientists to label images, but more complex, real-world projects often require hiring an AI video annotation service provider.
The annotation tools offer different sets of features to annotate single or multiple frames efficiently. Labels are applied to the objects using any of the annotation techniques explained below within an image; the number of labels on each image may vary, depending upon the use case.
How to Annotate Images?
In general, this is how image annotation works:
- Step #1: Prepare your image dataset.
- Step #2: Specify the class labels of objects to detect.
- Step #3: In every image, draw a box around the object you want to detect.
- Step #4: Select the class label for every box you drew.
- Step #5: Export the annotations in the required format (COCO JSON, YOLO, etc.)
Free Image Annotation Tools
We tested the top free software tools for image annotation tasks. If you are looking for professional and enterprise image annotation solutions, we listed them further below.
Here is which free image annotation tool is the best for you:
Makingense.ai is a free online tool for labelling photos that does not require any software installation. You can use it with a browser, and it doesn’t need any complicated installations. Makes sense ai is built on the TensorFlow.js engine, one of the most popular frameworks for training neural networks.
While the tool provides basic functionality that is easily accessible, it provides a good alternative for fast image annotation testing and small projects. The web-based image annotation tool MakeSense.AI is free to use under the GPLv3 license. GitHub Stars: 1.8k
- No installation is required; the tool is fully online.
- Makesense.ai supports multiple annotation shapes.
- Fast way to annotate a picture or a set of photos without installing software.
- A good option for beginners, this annotation tool walks the user through the annotation process.
- The annotation tool features a modern interface and new, time-saving add-ons that are appealing for large datasets.
CVAT – Computer Vision Annotation Tool
Developed by Intel researchers, CVAT is a popular open-source tool for image annotation. GitHub Stars: 5.7k
- This annotation tool requires some manual installation as it is based on Github.
- Once it is set up, it provides more tools and features than others, for example, shortcuts and a label shape creator.
- CVAT supports add-ons like TensorFlow Object Detection and Deep Learning Deployment Toolkit.
- The computer vision application platform Viso Suite includes CVAT for businesses.
Written in Python, LabelImg is a popular barebones graphical image annotation tool. GitHub Stars: 14.7k
- The installation is relatively simple and is generally done through a command prompt/terminal.
- The image annotation tool is great for datasets under 10,000 images, as it requires a lot of manual interaction and is made to help annotate datasets for object detection models.
- The simple interface makes it easy to use what makes it a good tool for beginner ML programmers with many well-documented tutorials out there.
Business Image Annotation Solution
The computer vision platform Viso Suite includes a built-in image annotation environment based on CVAT . The entire Suite is cloud-native and accessible via any browser. Viso Suite provides an integrated image and video annotation solution for professional teams.
Users can collaboratively and seamlessly collect video data , annotate images, train and manage AI models, develop applications without coding, and operate large-scale computer vision systems.
Viso accelerates the entire application lifecycle end-to-end with automated no-code and low-code tools to automate and accelerate tedious integration tasks.
How long does Image Annotation take?
The time needed to annotate images greatly depends on the complexity of the images, the number of objects, the complexity of the annotations (polygon vs. boxes), and the required accuracy and level of detail.
Usually, even image annotation companies have a hard time telling how long image annotation takes before some samples have to be labeled to make an estimation based on the results. But even then, there is no guarantee that the annotation quality and consistency allow precise estimations. While automated image annotation and semi-automated tools help to accelerate the process, there is still a human element required to ensure a consistent quality level (hence “supervised”).
In general, simple objects with fewer control points (window, door, sign, lamp) require far less time to annotate compared to region-based objects with more control points (fork, wineglass, sky). Tools with semi-automatic image annotation and preliminary annotation creation with a deep learning model help to speed up both the annotation quality and speed.
Read our article about CVAT , a tool that provides semi-automatic image annotation features.
Types of Image Annotation
Image annotation is frequently used for image recognition , pose estimation, keypoint detection, image classification, object detection, object recognition, image segmentation, machine learning, and computer vision models. It is the technique used to create reliable datasets for the models to train on and thus is useful for supervised and semi-supervised machine learning models.
For more information on the distinction between supervised and unsupervised machine learning models, we recommend Introduction to Semi-Supervised Machine Learning Models and Self-Supervised Learning: What It Is, Examples and Methods for Computer Vision . In those articles, we discuss their differences and why some models require annotated datasets while others don’t.
The purposes of image annotation (image classification, object detection, etc.) require different techniques of image annotation in order to develop effective datasets.
1. Image Classification
Image classification is a type of machine learning model that requires images to have a single label to identify the entire image . The image annotation process for image classification models aims at recognizing the presence of similar objects in images of the dataset.
It is used to train an AI model to identify an object in an unlabeled image that looks similar to classes in annotated images that were used to train the model. Training images for image classification is also referred to as tagging. Thus, image classification aims to simply identify the presence of a particular object and name its predefined class.
An example of an image classification model is where different animals are “detected” within input images. In this example, the annotator would be provided with a set of images of different animals and asked to classify each image with a label based on the specific animal species. The animal species, in this case, would be the class, and the image is the input.
Providing the annotated images as data to a computer vision model trains the model for the unique visual characteristic of each type of animal. Thereby, the model would be able to classify new unannotated animal images into the relevant species.
2. Object Detection and Object Recognition
Object detection or recognition models take image classification one step further to find the presence, location, and the number of objects in an image. For this type of model, the image annotation process required boundaries to be drawn around every detected object in each image, allowing us to locate the exact position and number of objects present in an image. Therefore, the main difference is that classes are detected within an image rather than the entire image being classified as one class (Image Classification).
The class location is a parameter in addition to the class, whereas in image classification, the class location within the image is irrelevant because the entire image is identified as one class. Objects can be annotated within an image using labels such as bounding boxes or polygons.
One of the most common examples of object detection is people detection . It requires the computing device to continuously analyze frames to identify specific object features and recognize present objects as persons. Object detection can also be used to detect any anomaly by tracking the change in the features over a certain period of time.
3. Image Segmentation
Image segmentation is a type of image annotation that involves partitioning an image into multiple segments. Image segmentation is used to locate objects and boundaries (lines, curves, etc.) in images. It is performed at the pixel level , allocating each pixel within an image to a specific object or class. It is used for projects requiring higher accuracy in classifying inputs.
Image segmentation is further divided into the following three classes:
- Semantic segmentation depicts boundaries between similar objects. This method is used when great precision regarding the presence, location, and size or shape of the objects within an image is needed.
- Instance segmentation identifies the presence, location, number, and size or shape of the objects within an image. Therefore, instance segmentation helps to label every single object’s presence within an image.
- Panoptic segmentation combines both semantic and instance segmentation. Accordingly, panoptic segmentation provides data labeled for background (semantic segmentation) and the object (instance segmentation) within an image.
4. Boundary Recognition
This type of image annotation identifies lines or boundaries of objects within an image. Boundaries may include the edges of a particular object or regions of topography present in the image.
Once an image is properly annotated, it can be used to identify similar patterns in unannotated images. Boundary recognition plays a significant role in the safe operation of self-driving cars.
In image annotation, different annotation shapes are used to annotate an image based on the selected technique. In addition to shapes, annotation techniques like lines, splines, and landmarking can also be used for image annotation.
The following are popular image annotation techniques that are used based on the use case.
1. Bounding Boxes
The bounding box is the most commonly used annotation shape in computer vision. Bounding boxes are rectangular boxes used to define the location of the object within an image. They can be either two-dimensional (2D) or three-dimensional (3D).
Polygons are used to annotate irregular objects within an image. These are used to mark each of the vertices of the intended object and annotate its edges.
This is used to identify fundamental points of interest within an image. Such points are referred to as landmarks or key points. Landmarking is significant in face recognition .
4. Lines and Splines
Lines and splines annotate the image with straight or curved lines. This is significant for boundary recognition to annotate sidewalks, road marks, and other boundary indicators.
Image annotation is the task of annotating an image with data labels. The annotation task usually involves manual work with computer-assisted help. Image annotation software such as the popular Computer Vision Annotation Tool CVAT help to provide information about an image that can be used to train computer vision models.
If you need a professional image annotation solution that provides enterprise capabilities and automated infrastructure, have a look at Viso Suite . The end-to-end computer vision platform covers not only image annotation, but also the related upstream and downstream tasks. Those cover data collection, model management, application development, DevOps, and Edge AI capabilities. Get a demo for your organization .
If you want to learn more about Computer Vision, I recommend reading the following articles:
- What is Computer Vision? Everything you need to know
- Explore the most popular Computer Vision tools
- Read the full guide about Video Analytics
- A Beginner Guide to Generative Adversarial Networks (GANs)
Why Computer Vision Projects Fail (and How to Succeed)
The 10 Best Computer Vision Books in 2023
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A Complete Guide about Image Annotation
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Image annotation is crucial in computer vision, the field that enables computers to "see" and "understand" visual information just like humans.
Excellent artificial intelligence (AI) applications include self-driving cars, tumor detection, and uncrewed aerial aircraft. Without image annotation, most of these computer vision applications would be impossible. To build computer vision models, annotation, or annotation of images, is a crucial first step. Valuable machine learning and image recognition approaches rely on datasets.
What is Image Annotation?
Image annotation is the process of adding a layer of metadata to an image. It's a way for people to describe what they see in an image, and that information can be used for various purposes. For example, it can help identify objects in an image or provide more context about them. It can also provide helpful information on how those objects relate to each other spatially or temporally.
Image annotation tools allow you to create annotations manually or through machine learning algorithms (MLAs). The most popular MLA method currently used is called deep learning, which uses artificial neural networks (ANNs) to identify features within images and generate text descriptions based on those features.
Two common annotated image datasets are Google's OID (Open Images Database) collection and Microsoft's COCO Collection (Common Objects in Context), which each contain 2.5 million annotated instances in 328k images.
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How does Image Annotation work?
Images can be annotated using any open source or freeware data annotation tool. However, the most well-known open-source image annotation tool is the Computer Vision Annotation Tool (CVAT).
A thorough grasp of the type of data being annotated and the job at hand is necessary to select the appropriate annotation tool.
You should pay close attention to:
- The data's delivery method
- The necessary type of annotation
- The file type that annotations should be kept in
Several technologies can be utilized for annotations due to the enormous range in picture annotation jobs and storage formats. From basic annotations on open-source platforms like CVAT and LabelImg to complex annotations on large-scale data using technologies like V7.
Additionally, annotating can be carried out on an individual or group level, or it can be contracted out to independent contractors or businesses that provide annotating services.
An overview of how to begin annotating images is provided here.
1. Source your raw image or video data
This is the first step in any project, and it's essential to make sure that you're using the right tools. When working with image data, there are two main things you need to keep in mind:
- The file format of your image or video - whether it's jpeg or tiff; RAW (DNG, CR2) or JPEG.
- Whether you're working with images from a camera or video clips from a mobile device (e.g., iPhone/Android), there are many different types of cameras out there, each with its proprietary file formats. If you want to import all kinds of files into one place and annotate them, then start by importing only those formats that work well together (e.g., jpeg stills + h264 videos).
2. Find out what label types you should use
The type of task being used to train the algorithm has a direct bearing on the kind of annotation that should be used. For example, when an algorithm is being trained to classify images, the labels take the form of numerical representations of the various classes. On the other hand, semantic masks and border-box coordinates would be used as annotations if the system were learning image segmentation or object detection.
3. Create a class for each object you want to label
The next step is to create a class for each object you want to label. Each class should be unique and represent an object with distinct characteristics in your image. For example, if you’re annotating a picture of a cat, then one class could be called “catFace” or “catHead.” Similarly, if your image has two people in it, then one class could be labeled “Person1″and the other would be labeled “Person2″.
To do this correctly (and avoid making mistakes), we recommend using an image editor such as GIMP or Photoshop to create additional layers for each separate object you want to label separately on top of our original photo so that when we export these images later on they won't get mixed up with other objects from other photos.
4. Annotate with the right tools
The right tool for the job is imperative regarding image annotation. Some services support both text and image annotation, or just audio, or just video—the possibilities are endless. Using a service that works with your preferred communication medium is important.
There are also tools available for specific data types, so you should choose one that supports what you have in mind. For example: if you're annotating time series data (i.e., a series of events over time), you'll want a tool specifically designed for this purpose; if there isn't such a tool on the market yet, then consider building one yourself!
5. Version your dataset and export it
Once you’ve annotated the images, you can use version control to manage your data. This involves creating a separate file for each dataset version, including a timestamp in its filename. Then, when importing data into another program or analysis tool, there will be no ambiguity about which version is being used.
For example, we might call our first image annotation file “ImageAnnotated_V2”, followed by “ImageAnnotated_V3” when we make changes, and so on. Then, after exporting our final version of the dataset using this naming scheme (and saving it as a .csv file), it'll be easy enough to import back into Image Annotation later if needed.
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Tasks that need annotated data
Here, we'll take a look at the various computer vision tasks that necessitate the use of annotated image data.
Image classification is a task in machine learning where you have a set of images and labels for each image. The goal is to train a machine learning algorithm to recognize objects in images.
You need annotated data for image classification because it’s hard for machines to learn how to classify images without knowing what the correct labels are. It would be like going blindfolded into a room with 100 objects, picking up one at random, and trying to guess what it was -- you'd do much better if someone showed you the answers beforehand.
Object detection & recognition
Object detection is the task of finding specific objects in an image, while object recognition involves identifying those objects. Finding a thing that you have not seen before is known as novel detection, while recognizing an object that you have seen previously is known as familiar detection.
Object detection can be further divided into bounding box estimation (which finds all the pixels that belong to one object) and class-specific localization (which determines which pixel belongs to which class). Specific tasks include:
- Identifying objects in images.
- Estimating their location.
- Estimating their size.
Image segmentation is the process of splitting an image into multiple segments. This can be done to isolate different objects in the image or to isolate a particular object from its background. Image segmentation is used in many industries and applications, including computer vision and art history.
Image segmentation has several benefits over manual editing: it's faster and more accurate than hand-drawn outlines; it doesn't require additional training time; you can use one set of guidelines for multiple images with slightly different lighting conditions; automated algorithms don't make mistakes as quickly as humans do (and when they do make mistakes, they're easier to fix).
Semantic segmentation is the process of labeling each pixel in an image with a class label. This might seem similar to classification, but there is an important distinction: classification assigns a single label (or category) to an entire image; semantic segmentation gives multiple labels (or categories) to individual pixels within the image.
Semantic segmentation is a type of edge detection that identifies spatial boundaries between objects in an image. This helps computers better understand what they’re looking at, allowing them to categorize new images and videos better as they come across them in the future. It's also used for object tracking — identifying where specific objects are located within a scene over time — and action recognition — remembering actions performed by people or animals in photos or videos.
Instance segmentation is a type of segmentation that involves identifying the boundaries between objects in an image. It differs from other segmentation types in that it requires you to determine where each object begins and ends, rather than simply assigning a single label to each region. For example, if you were given an image with multiple people standing next to their cars at a parking lot exit, instance segmentation would be used to determine which car belonged to which person and vice versa.
Instances are often used as the input features for classification models because they contain more visual information than standard RGB images. Additionally, they can be processed easily since they only require grouping into sets based on their common properties (i.e., colors) rather than performing optical flow techniques for motion detection.
Panoptic segmentation is a technique that allows you to see the data from multiple perspectives, which can be helpful for tasks such as image classification, object detection and recognition, and semantic segmentation. Panoptic segmentation is different from traditional deep learning approaches in that it does not require training on the entire dataset before performing a task. Instead, panoptic segmentation uses an algorithm to identify which parts of an image are important enough to use when deciding what information is being collected by each pixel in the image sensor.
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Business Image Annotation Solution
Business image annotation is a specialized service. It requires specialized knowledge and experience. It also requires special equipment to perform the annotation. Therefore, you should outsource this task to a business image annotation partner.
Viso Suite, a computer vision platform, has a CVAT-based image annotation environment as part of its core functionality. The Suite is built for the cloud and can be accessed from any web browser. The Viso Suite is a comprehensive tool for professional teams to annotate images and videos. Collaborative video data collection, image annotation, AI model training and management, code-free application development, and massive computer vision infrastructure system operations are all possible.
Through the use of no-code and low-code technologies, Viso can speed up the otherwise slow integration process across the board in the application development lifecycle.
How long does Image Annotation take?
Timing for an annotation relies heavily on the quantity of data needed and the intricacy of the annotation itself. For example, annotations that contain only a few items from a few different classes can be processed far more quickly than those that have objects from thousands of classes.
Annotations that only need the image itself annotated can be completed more quickly than ones that involve pinpointing several objects and key points.
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How to find quality image data?
It is challenging to gather high-quality annotated data.
Annotations must be built from raw acquired data if data of a certain kind is not freely available. This usually entails a set of tests to rule out any possibility of error or taint in the processed data.
The quality of image data is dependent on the following parameters:
- Number of annotated images: The more annotated images you have, the better. In addition, the larger your dataset is, the more likely it will be to capture diverse conditions and scenarios that can be used for training.
- Distribution of annotated images : A uniform distribution among various classes isn't necessarily desirable because it limits the variety available in your data set and, therefore, its utility. You'll want a lot of examples from each class so you can train a model that performs well under all circumstances (even if they're rare).
- Diversity in annotators : Annotators who know what they're doing can provide high-quality annotations with little error; one bad apple will ruin your whole batch! In addition, having multiple annotators ensures redundancy and helps ensure consistency across different groups or countries where there may be variations in terminology or conventions across regions.
Here are a few ways to obtain quality image data.
When it comes to image data, there are two main types: open and closed. Open datasets are freely available for download online, with no restrictions or licensing agreements. Closed datasets, on the other hand, can only be used after applying for a license and paying a fee—and even then, may require additional paperwork from the user before being given access.
Some examples of open datasets include Flickr and Wikimedia Commons (both are collections of photos contributed by people all over the world). In contrast, measures of closed datasets include commercial satellite imagery sold by companies like DigitalGlobe or Airbus Defence & Space (these companies offer high-resolution photos but require extensive contracts).
Scrape web data
Web scraping is the process of searching the internet for specific types of photos using a script that automatically does many searches and downloads the results.
The data obtained by online scraping is usually in a very raw state and requires extensive cleaning before any algorithm or annotation can be conducted, yet it is easily accessible and quick to collect. For example, using scraping, we can assemble photos that are already tagged as belonging to a specific category or subject area based on the query we provide.
Classification, which only needs a single tag for each image, is greatly facilitated by this annotation.
Self annotated data
Another type of data is self-annotated. In this case, the owner of the data has manually labeled it with their labels. For example, you may want to annotate images of cars and trucks with their current model year. You can scrap images from manufacturer websites and match them with your dataset using a tool like Microsoft Cognitive Services.
This type of annotation is more reliable than crowdsourced labeling because humans are less likely to mislabel or make mistakes when they’re annotating their data than when they are labeling someone else's data. However, it also costs more—you have spent money on human labor for these annotations.
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Types of Image Annotation
Image annotation is a process of adding information to an image. Many types of annotations can be applied to an image, such as text annotations, handwritten notes, geotags, etc. Below we will discuss some of the most common types of annotated images:
1. Image Classification
Image classification is a process of assigning a class label to an image. An image classifier is a machine learning model that learns to classify images into different categories. The classifier is trained on a set of labeled images and is used to classify new images.
Classification has two types: supervised and unsupervised. Supervised classification uses training data with labels, while unsupervised does not use labeled data but instead learns on its own from unlabeled examples in the dataset.
2. Object Detection and Object Recognition
Object detection is the process of finding objects in an image. This includes determining whether there are any objects or not, what they are, where they are located, and how many there are. Object recognition is identifying specific types of objects based on their appearance. For example, if we were looking at a picture containing elephants and giraffes (among other creatures), our goal would be to identify which ones were elephants and which were giraffes. These two tasks—object detection and object recognition—are often used together for greater accuracy; however, they can also be done independently. Object detection aims to ensure that everything in an image has been identified correctly (i.e., each dog has been labeled as a dog). The goal of object recognition is only partially concerned with labeling everything correctly; instead, it focuses on identifying specific types of things within an image (i.e., all dogs but not cats).
3. Image Segmentation
Segmenting an image involves dividing it into smaller, more manageable pieces. It is widely used in computer vision and image processing applications. Image segmentation can be used to identify objects in images and separate them from the background.
Image segmentation is further divided into three classes:
Semantic segmentation : Semantic segmentation represents the limits between conceptually equivalent things. This technique is employed if exact knowledge of an object's presence, position, size, or form inside a picture is required.
Instance segmentation: The objects in a picture are characterized by their existence, position, quantity, and size or form, all of which can be determined through instance segmentation. Thus, instance segmentation facilitates the identification of every object in an image.
Panoptic segmentation: Semantic and instance segmentation are combined in panoptic segmentation. For this reason, panoptic segmentation gives both semantic (background) and instance (object) labeled data.
4. Boundary Recognition
Boundary recognition is a type of image annotation, which means it’s used to describe the boundaries or edges in an image. It’s also called edge detection. Boundary recognition uses a mathematical algorithm to detect where edges are located in an image and then draw lines around them. This can help you segment images and identify objects within them.
Boundary recognition is used in many different applications, including object detection and object recognition, image classification, or just for your personal use as part of your workflow for annotating images with tags like “tagging faces” or “detecting buildings”.
Image annotation is the process of assigning attributes to a pixel or a region in an image. Image annotation can be done automatically, semi-automatically, or manually by humans. The annotation type depends on the use case, and it's essential to understand what kind of data you're trying to collect before choosing one technique over another. There are plenty of tools out there for doing this, ranging from simple online web apps to enterprise software solutions that integrate directly with your workflow management system (WMS).
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What is Image Annotation ?
September 19th, 2020 | by almir.
Years back, human efforts were used to carry out a lot of activities. They were needed in various sectors that use the human working capacity to carry out their businesses. Today, the world is experiencing a widespread technology revolution whereby information technology (IT) is fast dictating the pace and the scheme of events. With the use of computers, brilliant ideas have been transformed into excellent innovations like artificial intelligence and machine learning. These two innovations have made life, and business processes become easier. Machine learning and artificial intelligence rely on the use of a computing algorithm to replicate intelligent human behavior. These behaviour s include automatic speech recognition, augmented reality, and neural machine translations . That said, the success of these technological innovations in various sectors led to intensive research on the use of computers to visualize and interpret images. With different software, computer vision makes an effort to activate the machine eyes to see and interpret images .
Technology has proved that computer vision can give the human race and scientists autonomous vehicles, unmanned drones, and facial recognition . However, this extraordinary development can be enjoyed with the introduction of image annotation in the technology world. Image annotation is an important task when it comes to computer vision. As useful as this technology may be to the human race, there are lots of hidden information that are needed to be unraveled to understand its function fully and uses in the world. Therefore, today, I will be telling you all you need need to know about image annotation.
What is Image Annotation ?
Image annotation is an innovative computing technology where a human-powered task is used to manually identify and define region s in an image and also create a text-based description for the areas specified in the image. Image annotation catalyzes the pattern recognition process of the computer vision system when it is presented with a new image or data. The rate at which patterns or labels on images are being recognized differs. Images or data with similar labels are recognized easier and quicker than those with different labels. Image annotation technology is mostly used by artificial intelligence (AI) engineers to give information about an image for developing a computer vision model.
Different Techniques of Image Annotation
- 2D Bounding Box
The 2D bounding box technique is one of the significant techniques used in annotating images. In this method, annotators create a box around the object of interest at a particular frame and location. Also, you create place anchor points at the edges of each object. Many a time, the object may look the same. In this instance, you can draw boxes of all the objects in the image. Also, when there are different objects in the location, you must draw boxes around each object. For instance, if you have cars, bicycles, and pedestrians, you should draw boxes around each of them. After drawing the box, the annotator will choose labels that are a perfect fit to object in the box.
- 3D Bounding Box
The 3D bounding box, also known as cuboid , is a technique that is similar to the 2D bounding box. In this technique, the annotator creates a box around each image. They also place a point anchor point on the edges of each object. The boxes are created to cover a specific location and frame. However, the difference here is that the boxes can show the depth of the object been annotated .
- Polygon Annotation
Polygon annotation is an excellent image annotation technique annotator can use for objects that have irregular shapes and sizes . This method is useful because 2D and 3D bounding boxes can only annotate images with regular shapes. In this technique, polygons are created around the image of interest. This makes it easier to predict accurately the image’s volume and position within the polygonal space.
Polyline annotation is a fantastic annotation technique that is mainly used when you desire to make your computer vision system aware if annotating boundaries, splines, and lines . Annotators can also use the polyline technique to plan trajectories in drones . In this technique, straight or curved lines are created on images. Then the annotator would be left the option of annotating sidewalks, lanes, powerlines, and some other boundary indicators.
Keypoint tracking is an image annotation technique that annotators can use to determine the outermost part of an object. They also use it to determine the size and position of essential parts of the object. For instance, if you are annotating a car, it vital parts like side mirrors, headlights, and wheels are determined.
- Semantic Segmentation
If you wish to annotate image by dividing it into different segments or regions, you can choose semantic segmentation. For example, you can annotate the image of your car pack. A typical car pack comprises of trees, grasses, and sidewalk. Each of these components is separated into different segments. Then they are annotated separately. While using a semantic segmentation technique to carry out image annotation, you may need to adjust the threshold of the semantic segmentation algorithm. This will help it annotate any kind of image you desire.
Steps in Image Annotation
- Analyze Project Limitations
The first step to annotate a given image is to analyze the restriction on the project. Therefore, analyzing the project give annotators an idea about the project and its constraints.
- Use Appropriate tools
Many tools have been made available for annotators to use. However, you need to choose the right tool for the kind of image you want to annotate. The analysis you have previously done will assist you in choosing the best tool for a specific image.
- Use Appropriate Technique
After you have selected the right tool, you need to employ the correct technique to annotate a particular image. This involves studying the project instruction. Images produced with the proper technique can be used as training data.
Best Company that Provide Image Annotation Service
LabelOps is one of the best companies that provide excellent and fantastic imaging annotating services worldwide. It has the lowest hourly rates and best accurate annotations for the best training dataset. The company has a team of experts and professionals that specializes in machine learning, artificial intelligence, and image annotation . It also has state-of-the-art facilities that are used to carry out annotating services. LabelOps is an image annotating company that is certified and provides fantastic customer support services when you consult them for image annotating services.
The company has a track record of outstanding and excellent services on its previous and ongoing contracts. Their professional services are rendered to IT customers and other stakeholders at affordable prices.
Image annotation is vital to artificial intelligence engineers. Today, I have discussed crucial points you need to know about Image annotation. Please read through it and get informed about the image and text-based description technology.
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Best practices for successful image annotation
What is image annotation?
Image annotation is the task of labeling digital images, typically involving human input and, in some cases, computer-assisted help. Labels are predetermined by a machine learning (ML) engineer and are chosen to give the computer vision model information about the objects present in the image. The process of labeling images also helps machine learning engineers hone in on important factors in the image data that determine the overall precision and accuracy of their model.
Example considerations include possible naming and categorization issues, how to represent occluded objects (objects hidden by other objects in the image), how to deal with parts of the image that are unrecognizable, etc.
How do you annotate an image?
From the example image below, a person has used an image annotation tool to apply a series of labels by placing bounding boxes around the relevant objects, thereby annotating the image. In this case, pedestrians are marked in blue and taxis are marked in yellow, while trucks are marked in yellow.
Depending on the business use case and project, the number of image annotations on each image can vary. Some projects will require only one label to represent the content of an entire image (e.g. image classification). Other projects could require multiple objects to be tagged within a single image, each with a different label (e.g. a bounding box).
Image annotation software is designed to make image labeling as easy as possible. A good image annotation app will include features like a bounding box annotation tool and a pen tool for freehand image segmentation .
What are the different types of image annotation?
To create a novel labeled dataset for use in computer vision projects, data scientists and ML engineers have the choice between a variety of annotation types they can apply to images. Researchers will use an image markup tool to help with the actual labeling. The three most common image annotation types within computer vision are:
- Classification: With whole-image classification, the goal is to simply identify which objects and other properties exist in an image without localizing them within the image
- Object detection: With image object detection, the goal is to find the location (established by using bounding boxes) of individual objects within the image
- Image segmentation : With image segmentation, the goal is to recognize and understand what's in the image at the pixel level. Every pixel in an image is assigned to at least one class, as opposed to object detection, where the bounding boxes of objects can overlap. This is also known as semantic segmentation.
Whole image classification provides a broad categorization of an image and is a step up from unsupervised learning as it associates an entire image with just one label. It is by far the easiest and quickest to annotate out of the other common options. Whole-image classification is also a good option for abstract information such as scene detection or time of day.
Bounding boxes, on the other hand, are the standard for most object detection use cases and require a higher level of granularity than whole-image classification. They provide a balance between annotation speed and targeting items of interest.
Image segmentation is usually chosen to support use cases in a model where you need to definitively know whether or not an image contains the object of interest as well as what isn’t an object of interest. This is in contrast to other annotation types such as classification or bounding boxes, which may be faster but usually convey less information.
Why is image annotation useful?
Image annotation is a vital part of training computer vision models that process image data for object detection, classification, segmentation, and more. A dataset of images that have been labeled and annotated to identify and classify specific objects, for example, is required to train an object detection model.
This kind of computer vision model is an increasingly important technology. For example, a self-driving vehicle relies on a sophisticated computer vision image annotation algorithm. This model labels all the objects in the vehicle's environment, such as cars, pedestrians, bicycles, trees, etc. This data is then processed by the vehicle's computer and used to navigate traffic successfully and safely.
There are many off-the-shelf image annotation models available. One such model is YOLO, an object detection model that generates bounding box annotations in real time. YOLO stands for "You only look once," indicating that the algorithm analyzes the image and applies image annotations in one pass, prioritizing speed.
How does an AI data engine support complex image annotation?
Image annotation projects begin by determining what should be labeled in the images and then instructing annotators to perform the annotation tasks using an image annotation tool.
Annotators must be thoroughly trained on the specifications and guidelines of each image annotation project, as every company will have different image labeling requirements. The annotation process will also differ depending upon the image annotation tool used.
Once the annotators are trained on proper data annotation procedures for the project, they will begin annotating hundreds or thousands of images on an image annotation tool.
Data engine software like Labelbox is not only equipped with an image annotation tool, but also allows AI teams to organize and store their structured and unstructured data while providing a model training framework.
This scalable and flexible image annotation tool allows you to perform all the tasks mentioned above, from image classification to advanced semantic segmentation.
In addition, a best-in-class data engine will typically include additional features that specifically help optimize your image annotation projects.
An AI data engine helps users automate several parts of their image annotation process to accelerate efforts without diminishing the quality of annotations.
1. Automated queuing enables labelers to work continuously, eliminating the delays that occur as they wait to receive datasets, instructions, and other materials
2. Auto-segmentation tools that cut complex image segmentation drawing tasks down to seconds
3. Automate data operations and workflows programmatically with a Python SDK
4. AI teams can import model predictions as pre-labels, so that labelers can review and correct them instead of labeling data from scratch
This final labeling automation technique, called pre-labeling or model-assisted labeling, has been proven to reduce labeling time and costs by up to 50% for AI teams.
Pre-labeling decreases labeling costs as the model gets smarter with every iteration, leaving teams more time to focus on manually labeling edge cases or areas where the model might not be performing as well. It’s not only faster and less expensive, but delivers better model performance.
High-performance image annotation tools
The image annotation tool on the AI data engine you are testing can support a high number of objects and labels per image without sacrificing loading times.
Labelbox’s fast and ergonomic drawing tools provide efficiency to help reduce the time-consuming nature of creating consistent, pixel-perfect labels. A vector pen tool, for instance, allows users to draw freehand as well as generate straight lines. When you have the right tool for the job, image annotation is much easier.
Customization based on ontology requirements
Labelbox’s suite of image annotation tools gives you the ability to configure the label editor to your exact data structure (ontology) requirements, with the ability to further classify instances that you have segmented.
Ontology management includes classifications, custom attributes, hierarchical relationships, and more. You'll be able to quickly annotate images with the labels that matter to you, without the clutter of irrelevant options.
A streamlined user interface that emphasizes performance for a wide array of devices
An intuitive design helps lower the cognitive load on image labelers which enables faster image annotation. Moreover, an uncluttered online image annotation tool is built to run quickly, even on lower spec PCs and laptops. Both are critical for professional labelers who are working in an annotation editor all day.
Seamlessly connect your data via Python SDK or API
Stream data into your AI data engine and push labeled data into training environments like TensorFlow and PyTorch. Labelbox was built to be developer-friendly and API first, so you can use it as infrastructure to scale up and connect your computer vision models to accelerate labeling productivity and orchestrate active learning.
Benchmarks and consensus
Data quality is measured by both the consistency and the accuracy of labeled data. The industry-standard methods for calculating data quality are benchmarks (aka gold standard), consensus, and review.
An essential part of an AI data scientist’s job is figuring out what combination of these quality assurance procedures is right for annotated images used in your ML project. Quality assurance is an automated process that operates continuously throughout your training data development and improvement processes. With Labelbox consensus and benchmark features , you can automate consistency and accuracy tests. These tests allow you to customize the percentage of your data to test and the number of labelers that will annotate the test data.
Collaboration and performance monitoring
Having an organized system to invite and supervise all your labelers during an image annotation project is important for both scalability and security. An AI data engine should include granular options to invite users and review the work of each one.
With Labelbox, setting up a project and inviting new members is extremely easy, and there are many options for monitoring their performance , including statistics on seconds needed to label an image. You can implement several quality control mechanisms, including activating automatic consensus between different labelers or setting gold standard benchmarks.
Automatic annotation tool
If you are using image annotation to train a machine learning model, Labelbox allows you to use your model to create pre-labeled images for your labeling team using an automatic image segmentation tool .
Labelers can then review the output of the computer vision annotation tool and make any necessary corrections or adjustments. Instead of starting from scratch, much of the work is already done, resulting in significant time savings.
Final thoughts on image annotation with an AI data engine
The real-world applications for image annotation are endless, from content moderation to self-driving cars to security and surveillance. And, while there are many components to image annotation (classification, detection, segmentation), ultimately the annotation process itself is just a way to produce high quality data for model training.
When engineers at Tesla developed their Full Self Driving (FSD) vehicle technology in 2020, a key part of their success was an AI data engine. OpenAI currently uses their own proprietary AI data engine to train, deploy and maintain popular successful models such as GPT-3 and DALL-E 2.
From these examples, we can see how an AI data engine is key to deploying successful AI products, as it is the foundational infrastructure for how team members interface with data and models. Unfortunately, not all teams have the time and resources to architect an intricate and complex data engine for every use case.
Luckily, AI teams today don’t have to build and maintain data engines for their projects like Tesla and OpenAI did — they can invest in one instead. A best-in-class AI data engine gives you the ability to visualize, curate, organize, label data to improve model performance. Labelbox can help you get there.
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An overview: What is image annotation?
Image annotation is a significant task in computer vision. Considered to be one of the most important fields of machine learning and AI development, computer vision plays a crucial role.
It is the area of AI research that strives to give computers the ability to see and visually interpret the world. The applications of computer vision are huge ranging from medical diagnosis to autonomous vehicles.
In 2019, the global computer vision market valued at USD 13.75 billion, is expected to reach USD 24.03 billion by 2027, growing at a CAGR of 7.8% from 2020 to 2027. ( Source )
What is Image Annotation?
The task of annotating images with labels; would be short and suitable. Image annotation definition says that these labels enable machines to understand and interpret visual data like images and videos. Humans usually perform this task, and it takes a lot of time.
Labeling and annotation of visual data give way for efficient machine learning to enable computer vision capabilities.
Some semi-autonomous systems are available that reduce the task time by automatically labeling different aspects of images and video. This technique can be applied to many tasks in different fields. Depending on the application and the project, the number of labels on each image varies. These labels are usually predetermined by a computer vision scientist or a machine learning engineer.
Unlock the full potential of your images with precise annotations
Types of image annotation.
Image annotation meaning in simple terms is annotating the image with labels utilizing human skill sets. There are different techniques to annotate images with each technique having its own specific use.
Rectangular box annotation is one of the most commonly used types of annotation in localization and object detection tasks. It uses bounding boxes to define the location of an object, represented by its coordinates. Bounding boxes are frequently used in various tasks such as object detection to identify and locate objects accurately.
To define the shape and location of the target object in a more precise manner than rectangular boxes, complex polygons are used as not all objects can fit into a rectangular box due to their shape. This technique, known as polygonal segmentation, utilizes complex polygons for segmentation. This allows the capture of objects with an irregular shapes.
This is a pixel-wise annotation that involves assigning a label to every pixel in the image by separating the image into different regions. Every pixel, here, carries semantic meaning. The definition of the region is based on semantic information. For example, consider an autonomous vehicle that has to distinguish between the road and other paths/objects such as the sidewalk. Semantic segmentation can be used to differentiate between these regions.
In the bounding box, features like volume, position, etc. in a 3D space. Similar to bounding boxes, 3D cuboids provide additional depth information about the object. We get a 3D representation of the target object.
Autonomous vehicles utilize 3D cuboids to determine the distance between the car and any object in the surrounding environment.
Key-point and landmark
By creating dots across the image, we can identify shape variations and small objects. This is how key-point and landmarks are used. This type of annotation is useful for face recognition. By tracking multiple landmarks, we can easily recognize facial features and emotions.
This type of annotation involves the creation of lines and splines to delineate boundaries between different parts of an image. This is used for lane detection in autonomous vehicles.
Image annotation applications across industries
Image annotation service is used to teach machines to identify the different varieties of objects. Image annotation for machine learning is a growing reality in today’s market. Let’s check how image annotation is innovating various horizons across industries.
One of the common applications of image annotation is facial recognition. It involves extracting the relevant features from an image of a human face to distinguish images of one person or object from another.
Image annotation techniques, such as key-point and landmarks, enhance face recognition algorithms by frequently tracking different points in different parts of the face through track pointing.
The agriculture-technology industry has adopted image annotation techniques for various tasks, such as detecting plant diseases by recognizing images of both diseased and healthy crops, which can be achieved by utilizing bounding boxes or semantic segmentation types. This is one of the most basic uses of image annotation in agriculture technology .
In security systems, image annotation can flag items like suspicious bags in a particular area with the use of security cameras. By dividing the regions of a video into segments like restricted areas and not restricting the area using semantic segmentation, we can achieve efficient security. Image annotation enables the detection of suspicious activity.
Companies utilize image annotation to enhance product listings and ensure that customers find the products they are searching for. This is possible through semantic segmentation by tagging various components within search queries and product titles.
One of the major applications of image annotation is in robotics. It helps robots in distinguishing between different types of objects and helps in picking up the right object. To assist robots in distinguishing between different parts of a production line, line annotation can be utilized. It also is advantageous in restricting the robot in a particular area and not moving out of the intended zone.
The growth of Computer Vision & the need for image annotation
As the computer vision industry is advancing, the way of training data for each use case will keep evolving. As image annotation is one of the most important tasks in computer vision, getting annotation right, is essential. High-quality annotation work is important as it will finally affect the accuracy of identification between different objects. To evolve AI, it is necessary to train machines to identify and recognize visual data. However, annotating the same can be a tedious task for all stakeholders involved.
Now that there are service providers who can create data sets from scratch for a machine learning or computer vision program, image annotation outsourcing the same is surely proven to be a wise decision.
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Image Annotation for Computer Vision
A Guide to Labeling Visual Data for Your Machine Learning Project
The images you use to train, validate, and test your computer vision algorithms will have a significant effect on the success of your AI project. Each image in your dataset must be thoughtfully and accurately labeled to train an AI system to recognize objects similar to the way a human can. The higher the quality of your annotations, the better your machine learning models are likely to perform.
While the volume and variety of your image data is likely growing every day, getting images annotated according to your specifications can be a challenge that slows your project and, as a result, your speed to market. The choices you make about your image annotation techniques, tools, and workforce are worth thoughtful consideration.
We’ve created this guide to be a handy reference about image annotation. Feel free to bookmark and revisit this page if you find it helpful.
Read the full guide below, or download a PDF version of the guide you can reference later.
In this guide, we’ll cover image annotation for computer vision using supervised learning.
First, we’ll explain image annotation in greater detail, introducing you to key terms and concepts. Next, we’ll explore how image annotation is used for machine learning and some of the techniques that are available for annotating visual data, including images and videos.
Finally, we’ll share why decisions about your workforce are an important success factor for any machine learning project. We’ll give you considerations for selecting the right workforce, and you’ll get a short list of critical questions to ask a potential image annotation service provider.
Table of Contents
Introduction: will this guide be helpful to me, the basics: image annotation for machine learning, types of image annotation, image annotation techniques, your workforce for image annotation, questions to ask your image annotation service provider, cloudfactory and image annotation.
- Image Annotation Types
- Questions to Ask
- CloudFactory & Image Annotation
This guide will be helpful to you if:
- You have visual data (i.e., images, videos) from imaging technology that you want to prepare for use in training machine learning or deep learning models.
- You have annotated visual data but it does not meet your project’s quality requirements.
- You want to learn how you can use visual data to train high-performance machine learning or deep learning models.
What is image annotation?
In machine learning and deep learning, image annotation is the process of labeling or classifying an image using text, annotation tools, or both, to show the data features you want your model to recognize on its own. When you annotate an image, you are adding metadata to a dataset.
Image annotation is a type of data labeling that is sometimes called tagging, transcribing, or processing. You also can annotate videos continuously, as a stream, or frame by frame.
Image annotation marks the features you want your machine learning system to recognize, and you can use the images to train your model using supervised learning . Once your model is deployed, you want it to be able to identify those features in images that have not been annotated and, as a result, make a decision or take some action.
Image annotation is most commonly used to recognize objects and boundaries and to segment images for instance, meaning, or whole-image understanding. For each of these uses, it takes a significant amount of data to train, validate, and test a machine learning model to achieve the desired outcome.
- Simple image annotation may involve labeling an image with a phrase that describes the objects pictured in it. For example, you might annotate an image of a cat with the label “domestic house cat.” This is also called image classification , or tagging .
Complex image annotation can be used to identify, count, or track multiple objects or areas in an image. For example, you might annotate the difference between breeds of cat: perhaps you are training a model to recognize the difference between a Maine Coon cat and a Siamese cat. Both are unique and can be labeled as such. The complexity of your annotation will vary, based on the complexity of your project.
This image is an overview of the data types, annotation types, annotation techniques, and workforce types used in image annotation for computer vision.
What kind of images can be annotated for machine learning?
Images and multi-frame images, such as video, can be annotated for machine learning. Videos can be annotated continuously, as a stream, or frame by frame.
These are the most common types of data used with image annotation:
- 2-D images and video (multi-frame), including data from cameras or other imaging technology, such as a SLR (single lens reflex) camera or an optical microscope
3-D images and video (multi-frame), including data from cameras or other imaging technology, such as electron, ion, or scanning probe microscopes
How are images annotated?
You can annotate images using commercially-available, open source, or freeware data annotation tools . If you are working with a lot of data, you also will need a trained workforce to annotate the images. Tools provide feature sets with various combinations of capabilities, which can be used by your workforce to annotate images, multi-frame images, or video, which can be annotated as stream or frame by frame.
Are there image annotation services?
Yes; there are image annotation services. If you are doing image annotation in-house or using contractors, there are services that can provide crowdsourced or professionally-managed team solutions to assist with scaling your annotation process. We’ll address this area in more detail later in this guide .
There are four primary types of image annotation you can use to train your computer vision AI model.
Each type of image annotation is distinct in how it reveals particular features or areas within the image. You can determine which type to use based on the data you want your algorithms to consider.
1. Image Classification
Image classification is a form of image annotation that seeks to identify the presence of similar objects depicted in images across an entire dataset. It is used to train a machine to recognize an object in an unlabeled image that looks like an object in other labeled images that you used to train the machine. Preparing images for image classification is sometimes referred to as tagging .
Classification applies across an entire image at a high level. For example, an annotator could tag interior images of a home with labels such as “kitchen” or “living room.” Or, an annotator could tag images of the outdoors with labels such as “day” or “night.”
2. Object Recognition/Detection
Object recognition is a form of image annotation that seeks to identify the presence, location, and number of one or more objects in an image and label them accurately. It also can be used to identify a single object By repeating this process with different images, you can train a machine learning model to identify the objects in unlabeled images on its own.
You can label different objects within a single image with object recognition-compatible techniques, such as bounding boxes or polygons. For instance, you may have images of street scenes, and you want to label trucks, cars, bikes, and pedestrians. You could annotate each of these separately in the same image.
A more complex example of object recognition is medical imagery, such as CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans. This kind of data is multi-frame, so you can annotate it continuously, as a stream, or by frame to train a machine to identify features in the data, such as indicators of breast cancer. You also can track how those features change over a period of time.
A more advanced application of image annotation is segmentation. This method can be used in many ways to analyze the visual content in images to determine how objects within an image are the same or different. It also can be used to identify differences over time.
There are three types of segmentation:
a) Semantic segmentation delineates boundaries between similar objects and labels them under the same identification. This method is used when you want to understand the presence, location, and sometimes, the size and shape of objects.
You would use semantic segmentation when you want objects to be grouped, and it is typically reserved for objects you don’t need to count or track across multiple images, because the annotation may not reveal size or shape. For example, if you were annotating images that included both the stadium crowd and the playing field at a baseball game, you could annotate the crowd to segment the seating from the field.
b) Instance segmentation tracks and counts the presence, location, count, size, and shape of objects in an image. This type of image annotation is also referred to as object class . Using the same example of images of a baseball game, you could label each individual in the stadium and use instance segmentation to determine how many people were in the crowd.
You can perform either semantic or instance as pixel-wise segmentation, which means every pixel inside the outline is labeled. You can also perform them with boundary segmentation, where only the border coordinates are counted.
c) Panoptic segmentation blends semantic and instance segmentation to provide data that is labeled for both background (semantic) and object (instance). For example, panoptic segmentation can be used with satellite imagery to detect changes in protected conservation areas. This kind of image annotation can assist scientists who are tracking changes in tree growth and health to determine how events, such as construction or a forest fire, have affected the area.
In this series of photos (a) is the original image, and the others show three kinds of segmentation that can be applied in image annotation. In this example, the objects of interest are the cars and the people. Photo credit: Panoptic Segmentation , CVPR 2019
4. Boundary Recognition
Image annotation can be used to train a machine to recognize lines or boundaries of objects in an image. Boundaries can include the edges of an individual object, areas of topography shown in an image, or man-made boundaries that are present in the image. Annotated appropriately, images can be used to train a machine to recognize similar patterns in unlabeled images.
Boundary recognition can be used to train a machine to identify lines and splines , including traffic lanes, land boundaries, or sidewalks. Boundary recognition is particularly important for safe operation of autonomous vehicles. For example, the machine learning models used to program drones must teach them to follow a particular course and avoid potential obstacles, such as power lines .
It also can be used to train a machine to identify foreground from background in an image, or exclusion zones. For example, if you have images of a grocery store and you want to focus on the stocked shelves, rather than the shopping lanes, you can exclude the lanes from the data you want algorithms to consider. Boundary recognition is also used in medical images, where annotators can label the boundaries of cells within an image to detect abnormalities.
How do you do image annotation?
To apply annotations to your image data, you will use a data annotation tool . The availability of data annotation tools for image annotation use cases is growing fast. Some tools are commercially available, while others are available via open source or freeware. In most cases, you will have to customize and maintain an open source tool yourself; however, there are tool providers that host open source tools.
If your project and resources allow it, you may wish to build your own image annotation tool. This is generally the choice when existing tools don’t meet your requirements or when you want to build into your tool features that you value as intellectual property (IP). If you choose this route, be sure that you have the people and resources to maintain, update, and make improvements to the tool over time.
There are many excellent tools available today for image annotation . Some tools are narrowly optimized to focus on specific types of labeling, while others offer a broad mix of capabilities to enable many different kinds of use cases. Making the choice between a specialized tool or one with a wider set of features or functionality will depend on your current and anticipated image annotation needs. Keep in mind that there is no tool that can do it all, so you’ll want to choose a tool that you can grow into as your requirements change.
Image annotation involves one or more of these techniques, which are supported by your data annotation tool, depending on its feature sets.
These are used to draw a box around the target object, especially when objects are relatively symmetrical, such as vehicles, pedestrians, and road signs. It also is used when the shape of the object is of less interest or when occlusion is less of an issue. Bounding boxes can be two-dimensional (2-D) or three-dimensional (3-D). A 3-D bounding box is also called a cuboid.
This is an example of image annotation using a bounding box. The dog is the object of interest.
This is used to plot characteristics in the data, such as with facial recognition to detect facial features, expressions, and emotions. It also used to annotate body position and alignment, using pose-point annotations. In annotating images for sports analytics, for example, you can determine where a baseball pitcher’s hand, wrist, and elbow are in relation to one another while the pitcher throws the baseball.
This is an example of image annotation using landmarking. The eyes and nose are the features of interest.
This is pixel-level annotation that is used to hide areas in an image and to reveal other areas of interest. Image masking can make it easier to hone in on certain areas of the image.
This is used to mark each of the highest points (vertices) of the target object and annotate its edges: These are used when objects are more irregular in shape, such as houses, areas of land, or vegetation.
This is an example of image annotation using a polygon. The dog is the object of interest.
This plots continuous lines made of one or more line segments: These are used when working with open shapes, such as road lane markers, sidewalks, or power lines.
This is an example of image annotation using a polyline. The street’s lane line is the object of interest.
This is used to label and plot an object’s movement across multiple frames of video. Some image annotation tools have features that include interpolation , which allows an annotator to label one frame, then skip to a later frame, moving the annotation to the new position, where it was later in time. Interpolation fills in the movement and tracks, or interpolates, the object’s movement in the interim frames that were not annotated.
This is an example of image annotation using tracking. The car is the object of interest, spanning multiple frames of video.
This is used to annotate text in images or video when there is multimodal information (i.e., images and text) in the data.
This is a screenshot of an annotator’s view while labeling an image using transcription. The text in the image is the object of interest.
How are companies doing image annotation?
Organizations use a combination of software, processes, and people to gather, clean, and annotate images. In general, you have four options for your image annotation workforce. In each case, quality depends on how workers are managed and how quality is measured and tracked.
- Employees: These are individuals on your payroll, full-time or part-time. This option allows you to build in-house expertise and, typically, respond quickly to change. However, often those tasked with annotation were not hired to do annotation. It becomes an addition to their original job description, which means your employees are distracted from the reason you hired them in the first place. Additionally, scaling an internal team can be a challenge, as you bear the responsibility and expense of hiring, managing and training workers - as well as ensuring low churn.
- Contractors: These are temporary or freelance workers who you train to do the work. Their domain knowledge of your use case can increase over time, and they have the agility to incorporate changes quickly. With contractors, you often have the flexibility to scale your team up or down as needed. However, as with employees, you will bear the responsibility of management burdens and ensuring low worker churn.
- Crowdsourcing: This is an anonymous, ad hoc source of labor. You use a third-party platform to access large numbers of freelance workers at once, and typically users of the platform volunteer to do the work you describe. Domain knowledge, or even annotation experience, is limited, and you are never sure who is working on your data. Quality tends to be lower with crowdsourced teams because the workers are not vetted the same way they are with in-house, contracted, or managed teams.
- Managed teams: These are an outsourcing option. Teams are strategically selected, trained, and professionally managed individuals who work on teams. You share your requirements and annotation process, and they help you to scale it. Their understanding of domain knowledge with your use case is likely to increase over time, and they are likely to have the agility to incorporate changes to your image annotation process.
The advantages of outsourced, managed teams
There are three characteristics of outsourced, professionally managed teams that make them an ideal choice for image annotation, particularly for machine learning use cases.
1. Training and context
In image annotation, basic domain knowledge and contextual understanding is essential for your workforce to annotate your data with high quality for machine learning. Managed teams of workers label data with higher quality because they can be taught the context, or setting and relevance, of your data and their knowledge will increase over time. It’s even better when more than one member of your annotation team has domain knowledge, so they can manage the team and train new members on rules and edge cases. A managed team has staying power and can retain domain knowledge, which you do not get with crowdsourcing.
Machine learning is an iterative process. Your workflow and rules may change as you test and validate your models and learn from their outcomes. A managed team of annotators provides the flexibility to incorporate changes in data volume, task complexity, and task duration. The more adaptive your workforce is, the more machine learning projects you can work through. The best managed teams for image annotation can provide your team with valuable insights about data features - that is, the properties, characteristics, or classifications - that will be analyzed for patterns that help predict the target, or answer what you want your model to predict.
Managed image annotation teams can use technology to create a closed feedback loop with you that will establish reliable communication and collaboration between your project team and annotators. Workers should be able to share what they’re learning as they work with your data, so you can use their insights to adjust your approach.
Outsourced, managed teams are an ideal choice for image annotation. Similar to employees and contractors, managed teams bring all the benefits of an in-house team without placing the burden of management on your organization. Similar to crowdsourcing, managed teams can scale your workforce up or down quickly, based on your needs.
The best image annotation teams
If you are building machine learning models, the primary reason you will need an image annotation workforce is to achieve quality image annotation at scale . Using image data to train machine learning models requires a lot of data - in fact, high-performance machine learning and deep learning models require massive amounts of data labeled with high quality. For most AI project teams, that requires a human-in-the-loop approach.
The best image annotation teams are professionally managed teams that can provide:
- Expertise in image annotation - This kind of expertise comes with experience doing the many types of annotations described above, across multiple use cases, clients, and industries. Teams with expertise have developed processes and workflow best practices. They also know which annotation tool is best for a particular task or use case. Expertise is important to scaling your process. Teams with expertise understand how to transform complex tasks into distributed workflows that support high-quality image annotation.
- Quality - Your machine learning models will only be as good as the data that trains them. The best image annotation services monitor quality and can support, augment, or lead your team’s quality-assurance efforts. Their domain knowledge and proficiency with your rules, process, and use cases improves over time, as they work with your images and learn how you want to resolve edge cases. All of these contribute to higher quality image annotation and a better performing AI model.
- Agility - One constant in AI projects is change. Tasks, workflows, and use cases change. The best services have experience with many kinds of image annotation. Their teams can work with yours to manage task iterations as everyone learns during the process, so you can make improvements that increase throughput and quality. They also can make changes quickly to your image annotation process to counteract bias or to optimize your model’s performance.
If you need an image annotation workforce, you may be overwhelmed by the options available online. It can be challenging to evaluate image annotation services. Here are questions to keep in mind when you’re speaking with an image annotation service provider:
- What kind of images can your workforce annotate? How long has your workforce been annotating images?
- What types of annotations does your workforce have experience with? Does your workforce have experience annotating data in my specific domain? (e.g., medical, agriculture)
- What tools can your workforce use? What if we have built our own, proprietary image annotation tool - can you use that?
- How quickly can you scale the work? What kind of experience does your team have with a project like this?
- What standard do you use to measure quality?
- What processes are in place to ensure high quality throughout the annotation process?
- How do you share quality metrics with our team? What happens when quality measures aren’t met?
- If workers change, who trains new team members? Describe how you transfer context and domain knowledge as individuals transition on or off our image annotation team.
- How will our team communicate with your data labeling team?
- How does your team handle changes to our annotations or workflow? How quickly can changes be incorporated into our process?
- Can you scale my image annotation work up or back, per our needs?
- What is your pricing model (e.g., per annotation, task, hour)?
- Can we pay month to month or is it an annual contract?
- How do changes in the scale of the work, task definition, or project scope change pricing for our project? Can we revise task instructions without renegotiating our contract?
- Do I have to maintain a certain volume or throughput to retain the pricing in my contract? Do we need to renegotiate our contract or incur additional fees if throughput changes?
At CloudFactory, we have a decade of experience professionally managing image annotation teams for organizations around the world. To every project, we bring:
We’ve worked on thousands of projects for hundreds of clients. We have a deep understanding of workforce training and management for image annotation. We can transform your successful process with as few as a handful or as many as thousands of remote workers. We bring a decade of experience to your project and know how to design workflows that are built for scale. We’re tool-agnostic, so we can work with any tool on the planet, even the ones you build yourself.
Our professionally managed, team approach ensures increased domain knowledge and proficiency with your rules, process, and use cases over time. We monitor quality and can add layers of quality assurance (QA) to manage exceptions. We source tools that include robust workforce management features, quality control, and quality assurance options to meet your needs.
We have experience with a wide variety of tasks and use cases, and we know how to manage workflow changes. We put you directly in contact with a team lead, who works alongside the team and communicates with you via a closed feedback loop. This allows us to ensure task iterations, problems, and new use cases are managed quickly.
Together, we make a positive change.
At CloudFactory, we are on a mission to provide work to one million people in the developing world. We offer workers training, leadership, and personal development opportunities, including participation in community service projects. These experiences grow workers’ confidence, work ethic, skills, and upward mobility. Our clients and their teams are an important part of our mission .
Are you ready to learn how you can scale your image annotation process with an experienced workforce and great-fit tools? Find out how we can help you.
Reviewers Anthony Scalabrino , sales engineer at CloudFactory , a provider of professionally managed teams for image annotation for computer vision.
Tristan Rouillard and Alexander Wennman , who are co-founders at Hasty , an AI-powered image annotation tooling provider that offers tools for a wide variety of use cases and the flexibility to adapt the tool to support your workflow needs.
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Frequently asked questions.
In machine learning, image annotation is the process of labeling or classifying an image using text, annotation tools, or both to show the data features you want your ML model to recognize on its own. When you annotate an image, you are adding metadata to a dataset. Image annotation is a type of data labeling that is sometimes called tagging, transcribing, or processing.
By marking the features you want your machine learning system to recognize, you can use the images to train your model using supervised learning . Once your model is deployed, you want it to be able to identify those features in images that have not been annotated and, as a result, make a decision or take some action as a result.
What is an image annotation tool?
An image annotation tool is a software solution that can be used to label production-grade image data for machine learning. While some organizations take a do-it-yourself approach and build their own tools, there are many commercially-available image annotation tools, as well as open source and freeware tools. Some tools are narrowly optimized to focus on specific types of labeling, while others offer a broad mix of capabilities to enable many different kinds of use cases. Making the choice between a specialized tool or one with a wider set of features or functionality will depend on your current and anticipated image annotation needs.
What is Amazon Mechanical Turk image annotation?
Amazon Mechanical Turk is an online platform that allows you to access crowdsourced workers to do your image annotation work. You use Amazon’s platform to submit the image annotations you need, and Amazon’s platform distributes that work to anonymous workers. Also known as Amazon mTurk, this option is best for simple one-time projects when your tasks can be easily communicated in writing once, without having additional communication with annotators, and little to no domain expertise or experience is required.
Yes; there are image annotation services . If you are doing image annotation in-house or using contractors, there are services that can provide crowdsourced or managed-team solutions to assist with scaling your process. The best image annotation services can provide expertise, quality work, and agility to evolve tasks and use cases.
Where can I find image annotation software?
There are many excellent software tools available for image annotation . The tool you choose will be dependent on four things:
- The kind (e.g., image, video) of visual data you are working with;
- The dimension of that data (i.e., 2-D, 3-D); and
- How you want the tool to be deployed (e.g., cloud, container, on-premise)
- The feature sets you want your tool to have (e.g., dataset management, annotation methods, workforce management, data quality control, security)
What is an annotated image?
In machine learning, an annotated image is one that has been labeled using text, annotation tools, or both to show the data features you want your model to recognize on its own. When you annotate an image, you are adding metadata to a dataset. Image annotation is a type of data labeling that is sometimes called tagging, transcribing, or processing. You also can annotate videos continuously, as a stream, or by frame.
How can I do image annotation?
To do image annotation , you can use commercially-available, open source or freeware tools. If you are working with a lot of data, you likely will need a workforce to assist. Tools provide feature sets with various combinations of capabilities, which can be used to annotate visual data, including images and video. There are image annotation services that can provide crowdsourced or managed-team solutions to assist with scaling your process.
What is image annotation for machine learning?
Image annotation for machine learning is the process of labeling or classifying an image using text, drawing tools, or both to show the data features you want your model to recognize on its own. When you annotate an image, you are adding metadata to a dataset. Image annotation is sometimes called data labeling , tagging, transcribing, or processing. You also can annotate videos continuously, as a stream, or by frame.
How can I annotate images for deep learning?
To annotate images for deep learning , you can use commercially-available, open source or freeware tools. If you are working with a lot of data, you likely will need a workforce to assist. Tools provide feature sets with various combinations of capabilities, which can be used to annotate images or video. There are image annotation services that can provide crowdsourced or managed-team solutions to assist with scaling your process. The process of image annotation for machine learning and for deep learning are substantially the same, while the way algorithms are built and trained is different with deep learning.
What are ways to perform image annotation?
Image annotation involves using one or more of these techniques: bounding boxes, landmarking, masking, polygons, polylines, tracking, or transcription. Techniques will be supported by your annotation tool. Tools provide feature sets with various combinations of capabilities, which can be used by your workforce to annotate images or video. There are image annotation services that can provide crowdsourced or managed-team solutions to assist with scaling your process.
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What is Image Annotation?
Image annotation is the technique of adding additional information to an image so that computer vision models could use it to identify, tag, and categorize the image. It is the process of building datasets in order to train machine learning programs that can automatically assign captions or keywords to particular images. This helps the machine learning program (AI) learn how to identify elements within an image, much like how human brain capacity allows us to automatically identify an image’s subject (a person in a photo), as opposed to other objects, for example. Annotated images are frequently used to train image classification and object detection models, as well as for visualizing the output of such models.
Image annotation is an important building block for developing computer vision systems that can be used for applications such as image classification, image clustering, and augmented reality.
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What is image annotation? Introduction to image annotation for machine learning
Machine learning is an application of artificial intelligence that has had a profound impact on our everyday life by significantly improving speech recognition, traffic prediction, and online fraud detection, to name a few, on a massive scale. At its core, computer vision, an application of machine learning, enables machines to “see” and interpret the world around them, much like humans do.
The performance of your computer vision model highly depends on the quality and accuracy of its training data, which is essentially composed of annotations of images, video, etc.
Image annotation can be understood as the process of labeling images to outline the target characteristics of your data on a human level. The result is then used to train a model and, depending on the quality of your data, achieve the desired level of accuracy in computer vision tasks.
This blog post covers all you need to know about image annotation to make informed decisions for your business. Here are the questions this blog post will be covering:
What is image annotation?
What do you need to annotate images, what are the different types of image annotation, what are some image annotation techniques, how are companies doing image annotation, common image annotation use cases.
Image annotation is the practice of labeling images to train AI and machine learning models. It often involves human annotators using an image annotation tool to label images or tag relevant information, for example, by assigning relevant classes to different entities in an image. The resulting data also referred to as structured data, is then fed to a machine learning algorithm, which is often understood as training a model.
For example, you can ask your annotators to annotate vehicles in a given set of images. The resulting data can help you train a model that can recognize and detect vehicles and discriminate them from pedestrians, traffic lights, or potential obstacles on the road to navigate safely.
Autonomous driving is one example of how image annotation fuels computer vision. The use cases are countless, and we'll get back to them shortly, but first things first: What is it that you need to know before starting your annotation project?
Different image annotation projects may have slightly different requirements. However, diverse images, trained annotators, and a suitable annotation platform are the building blocks of every successful annotation project.
You need hundreds, if not upwards of thousands of images to train a machine learning algorithm that makes fairly accurate predictions. The more independent images you have, the more diverse and representative of the surrounding conditions they are, the better for you.
Suppose you want to train a security camera to detect crime activity or suspicious behavior. In this case, you will need images of the given street from different angles, in different lighting conditions to create a reliable model.
Make sure your images cover almost all possible conditions to guarantee precision in prediction results.
A team of trained and professionally managed annotators is necessary to drive an image annotation project to success. Establishing an effective QA (quality assurance) process and keeping communication open between the annotation service and key stakeholders is crucial for effective project execution. Providing the workforce with a clear annotation guideline is one of the best data labeling practices , too, since it helps them avoid mistakes before they are set for training.
Also, make sure you provide regular feedback to your workforce for a more effective QA process and create an environment where everyone feels encouraged to speak up and openly ask for help when needed. Try to provide as detailed feedback as possible and always keep in mind its influence on possible edge cases.
Suitable annotation platform
Behind every successful image annotation project is a functional and user-friendly annotation tool . When looking for an image annotation platform, make sure it has the tools needed to cover your ongoing use cases.
Need a grouping experience in the editor your annotators are using? Voice your concerns. Maybe that's something the creators behind the tool can provide you with the following product release. An integrated management system and quality management process are also necessary to track project progress and manage project quality.
Keep in mind that you may encounter technical issues, so make sure the image annotation platform you choose provides technical support through documentation and a dedicated 24/7 support team. In fact, that's a major reason why industry-leading companies trust SuperAnnotate with image annotation.
Quality for users
An efficient image annotation platform must be designed to minimize miscalculations or misplaced labels in the data. Ideally, it should upkeep remote user management while also streamlining and elevating the experience of those who are able to assess the annotators’ jobs.
An innovative and advanced annotation platform should lessen and detect human error, as well as foster the delivery of more annotated items in less time by automating complex annotation processes.
Moving forward, let's go over the categories of image annotation we often encounter. While the following types of annotation are different in essence, they are definitely not exclusive, and you may dramatically increase your model accuracy by combining them.
Image classification is a task that aims to get an understanding of an image as a whole by assigning it a label. All in all, it's the process of identifying and categorizing the class the image falls under as opposed to a selected object. As a rule of thumb, image classification applies to images where there is one object present.
Unlike image classification, where a label is assigned to an entire image, object detection is the practice of assigning labels to different objects in an image. As the name suggests, object detection identifies objects of interest within an image, assigns them a label, and determines their location.
When it comes to object detection tasks for computer vision, you can either train your own object detector with your own image annotations or use a pre-trained detector. Some of the more widely used approaches for object detection cover CNN, R-CNN, and YOLO .
Segmentation takes image classification and object detection a step further. This method consists of sectioning an image into multiple segments and assigning a label to each segment. In other words, pixel-level classification and labeling.
Segmentation is used to trace objects and margins in images and is commonly utilized for rather complex tasks that call for a more developed precision when sorting inputs. In fact, segmentation can be viewed as one of the most pivotal tasks in computer vision, which can be broken down into three sub-groups:
Semantic segmentation consists of dividing an image into clusters and assigning a label to every cluster. It is the task of collecting different fragments of an image and is considered a method of pixel-level prediction. There is basically no pixel that doesn't belong to a class in semantic segmentation.
To sum it up briefly, semantic segmentation can be understood as the process of classifying a specific aspect of an image and excluding it from the remaining image classes.
Instance segmentation is a computer vision task for sensing and confining a specific object from an image. It is a distinct practice of image segmentation as it mainly deals with identifying instances of objects and establishing their limits.
It is also very much relevant and heavily used in today’s ML world as it can cover use cases such as autonomous vehicles, agriculture, medicine, surveillance, etc. Instance segmentation identifies the existence, location, shape, and count of objects. You can use instance segmentation to point out how many people there are in an image, let's say.
Semantic vs. instance segmentation
Since semantic and instance segmentation are often confused, let's define the difference between them through an example.
Imagine we have an image of three dogs requiring image annotation. In the case of semantic segmentation, all of the dogs will belong to the same "dog" class, whereas instance segmentation will also provide them with unique instances, as three separate entities (despite being assigned the same label).
Instance segmentation is especially useful in cases where you're tasked with separately monitoring objects of similar type, which points pretty much explains the instance is one of the most challenging ones to comprehend out of the remaining segmentation techniques.
Panoptic segmentation is where instance segmentation and semantic segmentation meet. It classifies all the pixels in the image (semantic segmentation) and identifies which instances these pixels belong to (instance segmentation). In the panoptic segmentation task, you must categorize every pixel in the image as going to a class label, yet you also need to categorize which instance of that class they go with.
In our example, all the pixels in the image will be assigned labels, but each dog will be counted separately. In contrast to instance segmentation, every single pixel in panoptic segmentation has an exclusive label corresponding to the instance, which in turn means that no instances overlap.
There are a number of image annotation techniques, though not all of them will be applicable to your use case. Getting a firm grasp of the most common image annotation techniques is crucial to understanding what your project needs are and what kind of annotation tool to use to address those.
Bounding boxes are used to draw rectangles around objects such as furniture, trucks, and parcels, and it is, in general, more effective when such objects are symmetrical.
Image annotation with bounding boxes helps algorithms detect and locate objects, which is what the autonomous vehicle industry relies on, for example. Annotating pedestrians, traffic signs, and vehicles help self-driving cars navigate safely on the roads. Cuboids are an alternative to bounding boxes, with the only difference being that they are three-dimensional.
When it comes to functionality, bounding boxes make it significantly less complex for algorithms to catch what they're looking for in an image and subordinate the identified object with what they were initially skilled for.
Polylines are probably one of the easiest image annotation techniques to comprehend (along with the bounding box), as it is used to annotate line segments such as wires, lanes, and sidewalks. By using small lines joined at vertices, polylines are best at locating shapes of structures such as pipelines, rail tracks, and streets.
As you might have guessed, on top of the applications mentioned above, the polyline is fundamental for training AI-enabled vehicle perception models allowing cars to trace themselves in the large road schemes.
Polygons are used to annotate the edges of objects that have an often asymmetrical shape, such as rooftops, vegetation, and landmarks. The usage of polygons involves a very specific way of annotating objects, as you need to pick a series of x and y coordinates along the edges.
Polygons are often used in object detection and recognition models due to their flexibility, pixel-perfect labeling ability, and the possibility to capture more angles and lines when compared with other annotation techniques. Another important feature of polygon image annotation is the freedom that annotators have when adjusting the borders of a polygon to denote an object’s accurate shape whenever it is required. In this sense, polygons are the tool that best resembles image segmentation.
Key points are used to annotate very specific features on top of the target object, such as facial features, body parts, and poses. When using key points on a human face, you would be able to pinpoint the location of the eyes, nose, and mouth.
More specifically, it is commonly used for security purposes as it allows computer vision models to read and distinguish human faces quickly. This feature allows key-point annotation to be widely used in facial recognition use cases, emotion detection, biometric boarding, and so on.
Image annotation is a significant investment in your AI efforts that costs resources like time and money, so carefully consider your project size, budget, and delivery time before choosing how to carry out your image annotation project.
Here are three ways how image annotation could come off in your pipeline.
One way is managing your image annotation project with the resources available at hand. You can either have in-house annotators do the job or annotate yourself if it's a small-scale experimental project.
If you have a team of annotators, make sure there's also a QA process involved as, in this case, you share responsibility for errors in data. To avoid having an increasing number of errors and subsequently poor model performance, your annotators will need proper training, instruction, and expert guidance. So, if you're leaning towards a faster way to annotate images while maintaining a high labeling quality, consider outsourcing your project.
Leave it to the experts for the delivery of quality results on time. When outsourcing to image annotation service providers, you gotta be extra picky in the workforce to ensure they are well-trained, vetted, and professionally managed to save yourself more than a headache. Better yet, run a pilot project to evaluate the performance and see if the results are in line with your project objectives.
If your data is too niche-specific, say you have DICOM images that need medical expert annotators, the teams may lack subject-matter expertise. SuperAnnotate has got all of that covered, on top of putting the security of your datasets above everything. Too good to be true for a single platform? Let's go ahead and book your free pilot !
If you’re lacking resources, you can always crowdsource your image annotation project. Using crowdsourced solutions for computer vision or data labeling services is a commonly used method that is time-saving and affordable at scale. Sometimes the downside of this solution is insufficient or poorly organized quality control. In any case, make sure you keep communication open and provide consistent feedback if you decide to move on with this solution.
By now, we've explored how image annotation is being used to build technologies that you’re using in your everyday life; the applications can range from the simplest of activities, such as your iPhone being unlocked because it recognizes your face, to robots performing various tasks across different industries.
Let’s explore some of the most common use cases in the coming sections:
As we already touched upon, image annotation is used in developing facial recognition technology . It involves annotating images of human faces using key points to recognize facial features and distinguish between different faces.
As it is being further developed, face recognition technology is becoming more and more common in various areas, whether it be access control for our mobile devices, smart retail and personalized customer experiences, security and surveillance, or other sectors.
Security and surveillance
Another common image annotation application is surveillance to detect items such as suspicious bags and questionable behavior. Image annotation for security became extremely beneficial for the greater public as it took procedures such as crowd detection, night vision, and face identification for burglary uncovering to another level in the best way possible.
Agriculture technology relies on image annotation for various tasks, such as detecting plant diseases. This is done by annotating images of both healthy and diseased crops. Measuring a crop’s growth rate is one of the most important aspects of attaining prime harvests, and image annotation can now offer farmers timely observations of growth rates across massive areas.
Not only does this method save the farmers more time, but it can also save them more money, as it can help detect common issues in soil and vegetation in early stages; other issues may include nutrient deficiency, water shortage, bug issues, and poisonousness. AI-enable agriculture technology can also assess the ripeness of fruits and vegetables, which in return, can lead to more profitable harvests.
Image annotation has immense use in the medical field . For example, by annotating images of benign and malignant tumors using pixel-accurate annotation techniques, doctors can make faster and more accurate diagnoses.
Medical image annotation, in general, is being used to diagnose diseases such as cancer, brain tumors, or other nerve-related disorders. Here, annotators highlight the regions that need extra care, and this is done through the usage of bounding boxes, polygons, or whatever technique is applicable to that particular use case.
With the availability of data today, healthcare professionals are able to provide more accurate information to their patients, as predictive algorithms and image annotation techniques are now offering better predictive models.
Although humans are creating advanced technologies for robotics , automating a lot of human-involved processes, we still are in need of further assistance and cannot do everything on our own. Image annotation is helping robots to distinguish between various types of items which is realistic thanks to human input — annotators, in particular.
Line annotation is also of great importance in robotics, as it is being used to help differentiate between diverse fragments of a production line.
Robots depend on image annotation to perform tasks such as sorting parcels, planting seeds, and mowing the lawn, to name a few.
With the rising demand for autonomous vehicles, it goes without saying that the industry is rapidly expanding. How come? Through the appliance and assistance of data annotation techniques and labeling services. As the use of labeled data helps to make different objects more predictable by AI, annotation precision becomes the driving force for data-centric model creation. These high-quality annotated datasets are fed into the model/algorithms, iterated upon, and then — if spotted impurities — are reannotated, checked for quality (QA) after deployment and trained again to ensure the desired level of accuracy for autonomous vehicles.
Object detection and object classification algorithms are the ones responsible for autonomous vehicles’ ability to perform computer vision tasks and foster harmless decision-making. Because of these algorithms and labeled data, autonomous vehicles can easily recognize crossroads, provide emergency warnings, identify pedestrians and animals crossing the street, and even take control of the vehicle to help avoid accidents.
Despite the variety of image annotation techniques, only a few are actually applied to make training datasets in this sector. Bounding boxes, cuboids, lane annotation, and semantic segmentation are the main image annotation techniques that are used during the creation process. The latter assists the vehicle’s computer vision-based algorithm, which, in the end, makes scenarios easier to understand and contextualize for AI while also helping avoid possible collisions.
Nowadays, a large number of industries are moving ahead because of aerial/drone imagery. A drone’s main function is to collect data through sensors and cameras and use that same data to analyze information, and when extended to AI applications, it requires image and video annotations for training data. Aerial image annotation comprises labeling the images which were taken by the satellites/drones and then using them to train computer vision models to study the important characteristics of any particular domain.
The AI-enabled drone industry is involved in solving serious recurring issues in various spheres such as agriculture, construction, nature conversion, security and surveillance, fire detection, and much more. Each of these industries deserves an article of its own to cover all functionalities of drone imagery, so let’s narrow it down to one.
When zooming in on drone imagery functions for nature monitoring and conservation, for instance, the benefits seem endless. Researchers, conservationists, environmental engineers, and many others rely on drones to efficiently capture their preferred environmental data, which they later use to serve their project needs. One of the reasons why drones are preferred in this specific field is because of their efficiency in quickly gathering data which would otherwise require a human to fly out to destinations and take footage/pictures manually, making it both expensive and time-consuming.
Drones are used in a handful of ways to protect wild species and their habitats from going extinct, which usually encompasses annotated data of target areas and consecutive training. A more particular example is wildfire management to locate and detect fires to prevent them from causing further damage. AI-powered drones can detect fires way quicker than humans, come up with smarter and safer solutions, and prevent hazards before they become fatal.
Similar to the sectors mentioned above, the insurance industry was also highly influenced by AI and data annotation. When it comes to getting things done, both insurance workers and customers want fast results, and this is when AI enters the picture. AI’s ability to collect and analyze data takes a huge load off and makes inspection and evidence-gathering processes faster.
Another advantage is AI’s ability to fight against potential fraud through behavior analytics and pattern analysis. It is safe to say that AI risk management systems revolutionized insurance business models in terms of risk personalization as they effectively handle all the risk management of current and new insurance settlements. Such fraud detection applications can also detect any shortcomings with an application, which in turn makes it easier to spot irregular customer activities and behavior.
Artificial intelligence and machine learning are the driving forces of the modern tech environment, impacting all industries, from healthcare to agriculture, security, sports, and much more. Image annotation is one of the ways to create better and more reliable machine learning models, hence, more advanced technologies. So, the role of image annotation cannot be overstated.
Remember that your machine learning model is as good as your training data. So if you have a large amount of accurately labeled images, videos, or just any data, you can build a model that delivers excellent results and serves humans for the better.
Now that you know what image annotation is, the different image annotation types, techniques, and use cases, you can take your annotation project or model creation to the next level. Are you ready to get started?
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It’s pretty well known that machine learning (ML) is deeply involved in advanced technologies like autonomous vehicles, robotics, drones, medical imaging, and security systems. But what many don’t know is the key driver that brings many of these technologies to life — called image annotation. It is one of the most important components of computer vision and image recognition common in the inner-workings of these exciting fields.
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What Is Image Annotation?
Image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords in a digital image. Data labelers use tags , or metadata, to identify characteristics of the data fed into an AI or ML model to learn to recognize things the way a human would. Tagged images are then used to train the algorithm to identify those characteristics when presented fresh, unlabeled data.
Image annotations are important drivers of computer vision algorithms because they form the training data that is input to supervised learning . If the annotations are of high quality, the model will “see” the world and create accurate insights for the application. If they are low quality, ML models will not provide a clear picture of relevant real-world objects and will not perform well. Annotated data is particularly important when the model is trying to solve a new field or domain.
Types of Image Annotation
There are several key forms of algorithm-based image annotation methods that are used by ML engineers.
Bounding Box Annotation
Entails making a rectangular drawing of lines from one corner of an object to another in an image, based on its shape.
Boundaries of an item in a frame are annotated with high precision, allowing the object to be identified with the right size and form. Polygon annotation is common for recognizing things like street signs, logo images, and facial recognition.
This 3D type of annotation involves high-quality labeling and marking to highlight 3D drawing forms. It is used to determine the depth or distance of items from things like buildings or cars and helps identify space and volume, so it’s common in construction and medical imaging.
Language can be very difficult to interpret, so text annotation helps create labels in a text document to identify phrases or sentence structures. It helps prepare datasets for training so that the model can understand language, purpose, and even emotion behind the words.
Also known as picture segmentation, this type groups sections of an image that are part of the same object class. Pixels in an image are categorized to create a pixel-level prediction.
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Use Cases for Image Annotation
With the help of digital photos, videos and ML models, computers can learn to understand visual environments as humans do. High-quality annotations help drive the accuracy of computer vision models that are used in an increasingly wide range of applications .
ML algorithms for autonomous cars must of course be able to recognize things like road signs, traffic lights, bike lanes, and other potential road risks like bad weather. Picture annotation is common in various areas, such as advanced driver-assistance systems (ADAS), navigation and steering response, road object (and dimension) detection, and movement observations (such as with pedestrians).
Surveillance and Security
Security cameras are everywhere these days, and companies are throwing large sums into surveillance equipment to avoid theft, vandalism, and accidents. Image annotation is used in crowd detection, night and thermal vision, traffic motion and monitoring, pedestrian tracking, and face identification. ML engineers can train datasets for video and surveillance equipment using annotated photos to provide a more secure environment.
Even farmers are getting in on the game. Image annotation helps create content-driven data labeling to reduce human injury and protect crops. It also simplifies common agricultural tasks such as livestock management and the detection of unwanted or damaged crops.
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Key Challenges for Image Annotation in ML
While the benefits of deploying image annotation are plentiful, there are also a number of key challenges ML engineers and data science teams face.
Selecting the Right Annotation Tools
ML algorithms must be taught to recognize entities within digital visual images the way humans do. Organizations must understand what aspects of data types they want to use for data labeling, and they will need the right combination of digital annotation tools and a workforce that knows how to use them optimally.
Choosing Between Automated and Human Annotation
Using human resources to conduct image annotation, rather than computerized tools, can take more time and can add costs of finding the right engineers with the proper skillsets. Digital annotation performed with computerized tools provides a better level of accuracy and consistency.
Ensuring Quality Data Outputs
ML business models rely heavily on high-quality data outputs, but those ML models can only build precise projections if the data quality is indeed trusted. Subjective data can be hard for digital labelers to interpret depending on where they are geographically located, for example.
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7 Best Practices to Use While Annotating Images
Explaining data annotation for ml, challenges in the image annotation process for ml, use tight bounding boxes, tag or label occluded objects, maintain consistency across images, tag all objects of interest in each image, label objects of interests in their entirety, keep crystal clear labeling instructions, use specific label names in your images.
This is a guest article by tech writer Melanie Johnson
No matter how big or small your machine learning (ML) project might be, the overall output depends on the quality of data used to train the ML models. Data annotation plays a pivotal role in the process. And as we know it, it’s the process of marking machine-recognizable content using computer vision, or through natural language processing (NLP) in different formats, including texts, images, and videos.
Now, the primary function of data labeling is tagging objects on raw data to help the ML model make accurate predictions and estimations. That said, data annotation is key in training ML models if you want to achieve high-quality outputs. If the data is accurately trained, it won’t matter whether you deploy the model in speech recognition or chatbots, you will get the best results imaginable.
In this article, we are going to look at some of the best practices to use while annotating images for a computer vision project. This blog entry is particularly helpful to anyone who wants to
- understand the different image annotation types,
- learn about the challenges that data labelers encounter during image annotation for ML,
- know some of the best practices to use while annotating images for ML, including the pros and cons for each, and
- know what the future holds for data annotation as an industry.
So, if you have a ML project in mind or underway, you’ve come to the right place to get some profound insights and essentially everything you need to be well-versed in image annotation.
Data annotation follows a meticulous process of adding metadata to a dataset . This metadata is always in the form of tags, which then can be added to various data types like text, images, and video. The overarching idea when developing a training dataset for ML is to add comprehensive and consistent tags. Data scientists, in this case, understand the fundamental significance of using clean, annotated data to train ML models because it is the only way ML models recognize recurring patterns in annotated data. With enough processing of annotated data, an ML algorithm is able to recognize repetitive patterns when fed labeled data.
Therefore, it is the data annotator’s task to teach the ML model to interpret its environment, basically showing the ML model what output to predict. In other words, all necessary features within a dataset must be accurately labeled for the ML model to be able to recognize on its own and relate to unannotated data from a real-world environment.
Image annotation is central to Artificial Intelligence (AI) development in creating training data for ML. Objects in images are recognizable to machines through annotated images as training data, increasing the accuracy level of predictions. Before going deep into some of the difficulties data annotators face routinely during the image annotation process, it is important to know the various types of image annotation out there.
Bounding boxes . This is the commonly used image annotation type in computer vision. Bounding boxes appear in rectangular form and are then used to define the target object’s location. This annotation type has particular use in object detection and localization.
The example of using bounding boxes
Polygonal segmentation . Just like bounding boxes, polygons are sometimes used to define the shape and location of target objects in cases where the objects do not appear in rectangular shape. Complex polygons are commonly used in the annotation of images portraying sporting activities where target objects appear in different, complex shapes.
3D cuboids . The only difference between 3D cuboids and bounding boxes is the depth of information about the target object. 3D cuboids offer more, including 3D representation of the object, thus creating for machines distinguishable features such as position and volume in a 3D space.
Semantic segmentation . Semantic segmentation is literally the pixelwise annotation, i.e., using pixels in annotation, each pixel is then assigned to a class and carries a meaning. Examples of classes could be cars, traffic lights, or pedestrians. Semantic segmentation is important in use cases where the environmental context matters. For example, driverless cars rely on such information to understand their environments.
That in mind, image annotation is not an easy process, but demands in-depth knowledge and skills to accurately label data for ML training. The data labeling process, therefore, has its own fair share of challenges that data labelers face.
Below are some of the challenges this AI workforce faces from time to time.
Automated vs. human annotation . The cost of data annotation depends on the method used. The annotation using automated mechanisms promising a certain level of accuracy can be quick and less costly but risks precision in annotation because the degree of accuracy stays unknown until investigated. Human annotation, on the other hand, can take time and is costlier but more accurate.
Guaranteeing high-quality data with consistency . High-quality training data gives the best outputs to any ML model, and that is a challenge in itself. An ML model is only able to make accurate predictions if the quality of the data is good and also consistent. Subjective data, for example, is hard to interpret for data labelers coming from different geographical regions in the world due to differences in culture, beliefs, and even biases – and that can give different answers to recurring tasks.
Choosing the right annotation tool : Producing high-quality training datasets demands a combination of the right data annotation tools and a well-trained workforce. Different types of data are used for data labeling and knowing what factors to consider when picking the right annotation tool is important.
7 Best Practices for Annotating Images for ML
Now we know that only high-quality datasets bring about exceptional model performance. A model’s strong performance is attributed to an accurate and careful data labeling process that has been covered previously in this article. However, it’s important to know that data labelers deploy a few “tactics” that help sharpen the data labeling process for outstanding output. Note that every dataset demands unique labeling instructions for its labels With that in mind as you go through these practices, think of a dataset as an evolving phenomenon.
The secret behind using tight boxes around objects of interest is to help the model learn, accurately, which pixels count as relevant and which don’t. However, data labelers should be careful not to keep the boxes too tight to the extent of cutting off a portion of the object. Just make sure the boxes are small enough to fit the whole object.
What are occluded objects ? Sometimes an object can be partially blocked in an image and kept out of view, constituting an occlusion. If that is the case, ensure the occluded object is fully-labeled as if it were in full view. A common mistake in such cases is drawing bounding boxes on the partially visible part of the object. It is important to note that sometimes the boxes can overlap if there is more than one object of interest that appears occluded (which is okay); so that should not bother you as long as all objects are properly labeled.
The truth is, almost all objects of interest have some degree of sensitivity when identifying them, and that demands a high level of consistency during the annotation process. For example, the extent of damage to a vehicle body part to call it a “crack” must be uniform across all images.
Ever heard of false negatives in ML models? You see, computer vision models are made to learn what patterns of pixels in an image correspond to an object of interest. In this regard, every appearance of an object should be labeled in all images in order to help the model identify the object with precision.
One of the most basic and significant best practices when labeling images is ensuring the bounding boxes cover the whole object of interest. A computer vision model can be easily confused by what a full object constitutes if only a portion of an object is labeled. In addition to that, ensure there is completeness; in other words, all objects from all categories in an image should be labeled. Failure to annotate any object in an image hampers the ML model’s learning.
Since labeling instructions are not cast in stone, they should remain clear and shareable for future model improvements. Your fellow data labelers who might need to add more data to a dataset will rely on the set of clear instructions stacked safely somewhere to create and maintain high-quality datasets.
It’s strongly advised to be exhaustive and specific when giving an object a label name. In fact, it’s better to overly specific than less because it makes the relabeling process easier. If you are building a milk breed cow detector, for example, it is advisable to include a class for Friesian and Jersey even though every object of interest is a milk breed cow . In this case, all labels can be combined to be a milk breed cow if being too specific is an error, which is better than realizing too late that there exists individual milk breed cows and now you have to relabel the entire dataset.
Today’s innovators have embraced complex ML models with grit because they understand that high-quality data is all that matters. While there exists different types of image annotation, we have learned that the process of labeling images is possessed of a myriad of challenges; which, thankfully, data labelers have move beyond, or at least have learned to overcome some of them. Nonetheless, the elephant in the room has been how to make sure ML models perform at their optimum after the annotation process is complete. It is no secret, to this end, that the seven best practices discussed play a significant role in coming up with high-quality training datasets for ML models.
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Five types of image annotation and their use cases
Looking for information on the different image annotation types? In the world of artificial intelligence (AI) and machine learning, data is king. Without data, there can be no data science. For AI developers and researchers to achieve the ambitious goals of their projects, they need access to enormous amounts of high-quality data. In regards to image data, one major field of machine learning that requires large amounts of annotated images is computer vision .
What is computer vision?
Computer vision is one of the biggest fields of machine learning and AI development. Put simply, computer vision is the area of AI research that seeks to make a computer see and visually interpret the world. From autonomous vehicles and drones to medical diagnosis technology and facial recognition software, the applications of computer vision are vast and revolutionary.
Since computer vision deals with developing machines to mimic or surpass the capabilities of human sight, training such models requires a plethora of annotated images.
What is image annotation?
Image annotation is simply the process of attaching labels to an image. This can range from one label for the entire image, or numerous labels for every group of pixels within the image. A simple example of this is providing human annotators with images of animals and having them label each image with the correct animal name. The method of labeling, of course, relies on the image annotation types used for the project. Those annotated images, sometimes referred to as ground truth data, would then be fed to a computer vision algorithm. Through training, the model would then be able to distinguish animals from unannotated images.
While the above example is quite simple, branching further into more intricate areas of computer vision like autonomous vehicles requires more intricate image annotation.
What are the most common image annotation types?
Wondering what image annotation types best suit your project? Below are five common types of image annotation and some of their applications.
1. Bounding boxes
For bounding box annotation, human annotators are given an image and are tasked with drawing a box around certain objects within the image. The box should be as close to every edge of the object as possible. The work is usually done on custom platforms that differ from company to company. If your project has unique requirements, some companies can tweak their existing platforms to match your needs.
One specific application of bounding boxes would be autonomous vehicle development. Annotators would be told to draw bounding boxes around entities like vehicles, pedestrians and cyclists within traffic images.
Developers would feed the machine learning model with the bounding-box-annotated images to help the autonomous vehicle distinguish these entities in real-time and avoid contact with them.
2. 3D cuboids
Much like bounding boxes, 3D cuboid annotation tasks annotators with drawing a box around objects in an image. Where bounding boxes only depicted length and width, 3D cuboids label length, width and approximate depth.
With 3D cuboid annotation, human annotators draw a box encapsulating the object of interest and place anchor points at each of object’s edges. If one of the object’s edges are out of view or blocked by another object in the image, the annotator approximates where the edge would be based on the size and height of the object and the angle of the image.
Sometimes objects in an image don’t fit well in a bounding box or 3D cuboid due to their shape, size or orientation within the image. As well, there are times when developers want more precise annotation for objects in an image like cars in traffic images or landmarks and buildings within aerial images. In these cases, developers might opt for polygonal annotation.
With polygons, annotators draw lines by placing dots around the outer edge of the object they want to annotate. The process is like a connect the dots exercise while placing the dots at the same time. The space within the area surrounded by the dots is then annotated using a predetermined set of classes, like cars, bicycles or trucks. When assigned more than one class to annotate, it is called a multi-class annotation.
4. Lines and splines
While lines and splines can be used for a variety of purposes, they’re mainly used to train machines to recognize lanes and boundaries. As their name suggests, annotators would simply draw lines along the boundaries you require your machine to learn.
Lines and splines can be used to train warehouse robots to accurately place boxes in a row, or items on a conveyor belt. However, the most common application of lines and splines annotation is autonomous vehicles. By annotating road lanes and sidewalks, the autonomous vehicle can be trained to understand boundaries and stay in one lane without veering.
5. Semantic segmentation
Whereas the previous examples on this list dealt with outlining the outer edges or boundaries of an object, semantic segmentation is much more precise and specific. Semantic segmentation is the process of associating every single pixel in an entire image with a tag. With projects requiring semantic segmentation, human annotators will usually be given a list of pre-determined tags to choose from with which they must tag everything on the page.
Using similar platforms used in polygonal annotation, annotators would draw lines around a group of pixels they want to tag. This can also be done with AI-assisted platforms where, for example, the program can approximate the boundaries of a car, but might make a mistake and include the shadows underneath the car in the segmentation. In those cases, human annotators would use a separate tool to crop out the pixels that don’t belong. For example, with training data for autonomous vehicles, annotators might be given instructions like, “Please segment everything in the image by roads, buildings, cyclists, pedestrians, obstacles, trees, sidewalks and vehicles.”
Another common application of semantic segmentation is medical imaging devices. For anatomy and body part labeling, annotators are given a picture of a person and told to tag each body part with the correct body part names. Semantic segmentation can also be used for incredibly specialized tasks like tagging brain lesions within CT scan images.
Via semanticscholar.org, original CT scan (left), annotated CT scan (right).
Regardless of the type of image annotation or the use case, a partner you can trust to help get your next machine learning project off the ground is of tremendous value. Get started today by contacting our AI Data Solutions team.
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Annotating mockups & wireframes for accessibility, why annotate.
The university is required to produce IT that everyone in our community can use equally. By “shifting left” and ensuring that we take accessibility into account at the design stage of web and app projects, we facilitate compliance at the development stage. This results in a process that is:
Cheaper . It is less resource intensive to flag possible accessibility issues at the design stage. The alternative is having these issues crop up during development, or even worse, after development.
More efficient . Developers will appreciate the specificity of the annotations and be able to produce the IT much faster.
Educational . Accessibility is everyone’s job. UX practitioners should know that their designs have accessibility implications and what these are. Developers need to know how to produce accessible IT. Annotating designs bridges the gap between both. UX practitioners learn to specify how the design needs to be implemented to be accessible. In time, developers learn how to do so even in the absence of the annotations.
What to annotate
Though annotating designs is a very useful practice, there is a large percentage of the accessibility issues that cannot be annotated. Be sure developers know this and have them incorporate testing as they develop in order to catch issues that have not been accounted for.
Color and contrast
Color should not be used as the sole flag of meaning . Solution: Either change the design or annotate the design to direct the developer to add other markers (text, weight, decoration, etc).
Contrast between foreground and background of text, controls and graphics should meet specific ratios . If you spot anything that might be a contrast problem, it probably is. Solution: Annotate the design to ensure the developer has the information needed to meet this requirement. WebAIM has a good tool to help you analyze contrast, as well as a primer of the guidelines and the contrast ratios to be achieved.
Structure and meaning
To the naked eye, the design pretty much lays out the structure of the view. But we are also required to offer this structure to someone who cannot see and who is using screen reader software to navigate and read the IT. There are two complementary methods of achieving this:
Follow proper heading structure . The view needs to have a visual title. Depending on the visual structure, other subtitles may be necessary. In your annotations the main title should be an <h1>, subsequent titles will be <h2>, <h3>....<h6>. There should be no heading jumps or gaps. Solution: specify the heading levels in your design annotations.
Label the landmark regions of a view . Mentally slice and dice the view into semantic chunks by the intended function of the chunk. These could be something like branding, menu, footer, main content, sidebar, subsection, etc. All of these can be expressed semantically in the markup and it is your charge to help the developer to do so. These are the annotations they will need:
Solution: visually annotate the regions' boundaries and provide the correct label for each.
Images must have appropriate alternative text . If the image is meaningful, annotate it with alt=”the meaning of the image,” and if it is not meaningful with alt=””. Solution: Provide in design annotations the specific alternative text (e.g. 'Alt text="Universal design symbol"') for each image.
Text of links must meaningfully describe the destination . All links need to clearly and textually describe their function and also be unique within the view.
UI control text
User interface controls must also be labeled with text that meaningfully describes the function and is unique within the view.
Other semantic considerations
In general, all chunks that you can read visual meaning into need to have that meaning expressed in the markup; thus it is important to remind the developer of this. Here are some examples:
If it looks like a paragraph it needs to be code as one (<p>)
If it looks like a list, it needs to be coded as one (<ul> for bullets or <ol> for numbers) - note: some things do not really look like a list, but should be one. Any sequence can be construed as a list, even if it is horizontal.
Annotate tables to ensure the developer uses semantics
A column heading row <tr><th scope=”col”>Header label</th>....</tr>
A row heading on the row cell that could be the title of the row <th scope=”row”>
If there are a lot of tables in the same view, ensure that they have names by annotating with caption=”name of the table”
There is one rule: if an element is in a form, it needs to be a labeled form element, programmatically associated with a form element or with the form itself. This applies to
Form element labels <label for=”id of form element”>
Global instructions: either outside of the form or associated with it via <form aria-describedby=”id of global message container”>
Form element instructions <input aria-describedby=”id of information container”>
Error messages associated with form elements <input aria-describedby=”id of error message container”>
Form group titles: in complex forms, group like form elements with a title <fieldset><legend>Title of group</legend> …. Form elements (and their labels) </fieldset> This is required with radio and button groups, regardless of the complexity of the form.
Finally - if the design visually omits a form element label, annotate the form element with: aria-label=”name of this input”
Form element attributes:
Is the form element required? <input required>
Is it asking for something that the user has filled out already elsewhere? <input autocomplete=”on” name=”type of information needed”> - see complete list of possible values for later.
Is the input of a specific type? Is it asking for text, a number, a telephone, an email address, etc? Annotate with the corresponding type (full list in a Mozilla article about input types ): <input type=”email”>, <input type=”tel”>, <input type=”url”>
Some web pages will respond to user actions by changing the page without loading a new one. Users of screen readers will not perceive the change. Some strategies:
Add role=”alert” to alert the user to containers that have appeared
Add aria-live=”polite” to containers that change
Annotate with “pass focus to new input” if a user action has added a new form element
Evolve your annotation
Annotating designs for accessibility involves a bit of informed guess work and learning on the job. The more you do, the more secure you will be in your assumptions and the more on target will be your recommendations.
We have not scratched the surface of things that can be flagged in annotations to help developers do the right thing. The Figma Annotation plugin is actually a good learning tool, even if you do not use Figma.
If you are involved in reviewing the IT as it is being produced, a quick non-technical heuristic review will unearth many barriers that happen also to be the types of barriers that are really difficult to annotate against.
See these references for more discussion:
- Annotating designs for Accessibility (video) / Claire Webber and Sarah Pulis
- Top 5 Most Common Accessibility Annotations (article) / Deque
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How Companies Use Image Annotation to Produce High-Quality Training Data Image annotation is the foundation behind many Artificial Intelligence (AI) products you interact with and is one of the most essential processes in Computer Vision (CV).In image annotation, data labelers use tags, or metadata, to identify characteristics of the data you want your AI model to learn to recognize.
Image annotation is the process of labeling images in a given dataset to train machine learning models. When the manual annotation is completed, labeled images are processed by a machine learning or deep learning model to replicate the annotations without human supervision.
Image annotation is the practice of assigning labels to an image or set of images. A human operator reviews a set of images, identifies relevant objects in each image, and annotates the image by indicating, for example, the shape and label of each object. These annotations can be used to create a training dataset for computer vision models.
Image Annotation in 2023: Definition, Importance & Techniques. Image annotation is one of the most important stages in the development of computer vision and image recognition applications, which involves recognizing, obtaining, describing, and interpreting results from digital images or videos. Computer vision is widely used in AI applications ...
Image classification: An image annotation process that involves assigning class labels to an object of interest (e.g., people, plants, animals, etc.). Object recognition and detection: Also referred to as image recognition or object detection. The process involves finding if specific objects are present in the entire image.
Automatic image annotation. Output of DenseCap "dense captioning" software, analysing a photograph of a man riding an elephant. Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image.
Image annotation is frequently used for image recognition, pose estimation, keypoint detection, image classification, object detection, object recognition, image segmentation, machine learning, and computer vision models. It is the technique used to create reliable datasets for the models to train on and thus is useful for supervised and semi ...
Image annotation is the process of assigning attributes to a pixel or a region in an image. Image annotation can be done automatically, semi-automatically, or manually by humans. The annotation type depends on the use case, and it's essential to understand what kind of data you're trying to collect before choosing one technique over another.
Semantic Segmentation: Semantic segmentation is a pixel wise annotation, where every pixel in the image is assigned to a class. These classes could be pedestrian, car, bus, road, sidewalk, etc., and each pixel carry a semantic meaning. Semantic Segmentation is primarily used in cases where environmental context is very important.
Image annotation is an innovative computing technology where a human-powered task is used to manually identify and define regions in an image and also create a text-based description for the areas specified in the image. Image annotation catalyzes the pattern recognition process of the computer vision system when it is presented with a new ...
Image annotation is defined as the task of labeling digital images, typically involving human input and, in some cases, computer-assisted help. Labels are predetermined by a machine learning engineer and are chosen to give the computer vision model information about the objects present in the image. The process of labeling images also helps ...
Image annotation definition says that these labels enable machines to understand and interpret visual data like images and videos. Humans usually perform this task, and it takes a lot of time. Labeling and annotation of visual data give way for efficient machine learning to enable computer vision capabilities.
Image annotation is most commonly used to recognize objects and boundaries and to segment images for instance, meaning, or whole-image understanding. For each of these uses, it takes a significant amount of data to train, validate, and test a machine learning model to achieve the desired outcome.
Image annotation is the technique of adding additional information to an image so that computer vision models could use it to identify, tag, and categorize the image. It is the process of building datasets in order to train machine learning programs that can automatically assign captions or keywords to particular images. This helps the machine ...
Image annotation can be understood as the process of labeling images to outline the target characteristics of your data on a human level. The result is then used to train a model and, depending on the quality of your data, achieve the desired level of accuracy in computer vision tasks. This blog post covers all you need to know about image ...
Image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords in a digital image. Data labelers use tags, or metadata, to identify characteristics of the data fed into an AI or ML model to learn to recognize things the way a human would. Tagged images are then used to train the ...
This is the commonly used image annotation type in computer vision. Bounding boxes appear in rectangular form and are then used to define the target object's location. ... each pixel is then assigned to a class and carries a meaning. Examples of classes could be cars, traffic lights, or pedestrians. Semantic segmentation is important in use ...
Image annotation is simply the process of attaching labels to an image. This can range from one label for the entire image, or numerous labels for every group of pixels within the image. A simple example of this is providing human annotators with images of animals and having them label each image with the correct animal name. The method of ...
Image annotation is the human-powered task of annotating an image with labels. These labels are predetermined by the AI engineer and are chosen to give the computer vision model information about what is shown in the image. Depending on the project, the amount of labels on each image can vary. Some projects will require only one label to ...
Image descriptions. Images must have appropriate alternative text. If the image is meaningful, annotate it with alt="the meaning of the image," and if it is not meaningful with alt="". Solution: Provide in design annotations the specific alternative text (e.g. 'Alt text="Universal design symbol"') for each image.
Image annotation is the process of making an object in an image recognizable to machines through computer vision. And this process can be done by humans manually, using the tools and software ...