Image annotation has become an essential task in the field of computer vision. It enables machine learning systems to learn patterns and representations by discovering relationships between the image and its corresponding annotations. By establishing these relationships, the system can now generate annotation on new and unseen images. This is useful in real-time applications such as self-driving cars, robotics, security, etc.
In a nutshell, image annotation enables machines to understand the images by carefully analyzing them for tasks pertaining to object detection, segmentation, classification, etc.
In this blog, we will briefly explore what image annotation is, and we will discover the ways to perform image annotation.
What is Image Annotation?
Annotation in machine learning refers to a process of adding metadata or labels to the image with appropriate information. Usually, the information associated with the image is contextual and can include time, location, shape, size, color, and other attributes. The metadata completely depends on the task at hand. For instance, if the image is annotated for the purpose of developing an algorithm for self-driving, then the appropriate metadata would be the object class, such as cars, pedestrians, lane markings, object velocity, etc. For different applications, the metadata will be different.
Types of Image Annotation
Before moving on to the steps to perform image annotation, let’s briefly understand the types of image annotation. This will allow us to know which annotation suits the application and image.
- Bounding Box Annotation is a type of annotation that involves drawing a rectangular box around an object or region of interest in an image. This type of annotation is commonly used for object detection and localization tasks.
- Polygon annotation involves drawing a polygonal shape around an object or region of interest in an image. This type of annotation is commonly used for segmentation tasks, where the goal is to separate different objects or regions in an image.
- Point Annotation involves marking a specific point in an image. This annotation is used in face detection to mark important features such as eyes, lips, etc. This is also used to mark the joints in the body.
- Line Annotation is a type of annotation that involves drawing a line segment between two points in an image. This annotation type is commonly used for tasks such as road tracking and object orientation.
- Semantic Annotation involves masking an object of interest in an image. Each object is painted with a pixel mask to categorize the objects within the image.
- 3D Annotation is a type of annotation that involves annotating a 3D model with information such as shape, geometry, texture, etc.
- Landmark Annotation involves identifying and labeling specific landmarks or features on an object in an image. This can be used in face detection as well.
- Keypoint Annotation involves labeling specific points on an object or region of interest in an image. This type of annotation is commonly used for pose estimation and gesture recognition tasks.
- Text Annotation involves adding contextual text information to an image. It is very helpful in applications leveraging OCR.
- Image Captioning involves generating a textual description of an image. This type of annotation is commonly used for tasks such as image search, where the goal is to provide a textual description of the contents of an image.
- Video Annotation involves annotating individual frames in a video with additional information and context. This annotation type is commonly used for tasks such as object tracking, where the goal is to track the movement of an object over time.
- Time Series Annotation involves annotating a sequence of images with additional information and context. This type of annotation is commonly used for tasks such as gesture recognition and action recognition, where the goal is to recognize specific actions or movements over time.
Now let’s discuss some of the steps that will help you to annotate images easily and efficiently:
Choose an Annotation Platform
Choosing the right annotation platform is important because your productivity depends upon that. For instance, if the platform doesn’t offer a seamless experience and necessary tools, then you will have to compromise with the annotation task.
Consider the following factors when selecting an annotation platform:
- Compatibility– refers to the support that the platform offers. For instance, if you are working on medical images, the question arises, “Does the platform allow you to upload and work on DCIM format?”
- User Interface– UI is important as it enables you to navigate seamlessly to various tools and options, increasing your productivity.
- Features– generally refers to the tools that the platform offers. For instance, if you are annotating objects which require ultra-precision segmentation masks, the question arises, “Does the platform offer AI-powered polygon tools to increase preciseness and efficiency?”
- Cost– having many features typically comes with a high price. Remember to evaluate the task at hand and opt for a budget-friendly platform.
Import the Images
After selecting the annotation platform, the next step is to import the images you want to annotate. Most annotation tools allow you to import multiple images simultaneously. Ensure that the images are in the correct format, uniform size, and resolution for the annotation tool. Never compromise on the quality of images because an ML system will only be as good as the data fed into it.
Select the Annotation tool
As mentioned earlier, the platform must have various annotation tools. Why is this necessary? Let’s say you want to annotate a bridge; if you use a box annotation tool, you may add unnecessary objects. These objects will add noise to the data. To avoid such instances, specialized tools must be considered.
Remember, each object in the image must be treated differently. And the thumb rule is that while annotating, make sure that annotations are tightly bounded to the object of interest.
Annotate the Images
Once you have selected the annotation tool, the next step is to annotate the images. Follow these steps for accurate and efficient annotation:
- Zoom in on the image– Zooming in on the image can help you annotate more accurately.
- Choose the appropriate tool– Choose the appropriate tool for the selected annotation type. For example, if you are using bounding box annotation, select the bounding box tool.
- Annotate accurately– Draw the annotations accurately and avoid overlapping annotations.
- Label each annotation– Label each annotation with the appropriate class name, such as “car” or “tree.”
Verify the Annotations
After annotating the images, the next step is to verify the annotations. Verification involves checking the annotations for accuracy and completeness. Here are some tips for verifying annotations:
- Check for missed annotations– Ensure all the objects or areas of interest are correctly annotated.
- Check for accurate labeling– Ensure each annotation has been labeled with the correct class name.
- Check for consistent annotations– Ensure the annotations are consistent throughout the image set.
Export the Annotations
After verifying the annotations, the final step is to export the annotations in the desired format. Most annotation tools allow you to export annotations in formats such as XML, JSON, or CSV.
If you are outsourcing or crowding, these tips will play a major role because you can provide a blueprint of what you require. Do not hold back on the desired expectation when it comes to annotation. If you are thorough with the platform and tools it offers, you are well prepared to train or provide instructions per your requirements.
In this blog, we explored some easy steps to annotate images. It is necessary to opt for image annotation tools or platforms that offer the best services for your demands. These days most platforms offer AI image annotation services as well. This is extremely handy because it increases both your productivity and efficiency. Lastly, before choosing any platform, make sure that your task or goal is properly defined and articulated so that you won’t compromise on data annotation.