What is Image Annotation: Types, Tools, Use case  

Image notation is the backbone of modern computer vision systems. It bridges raw visual data to machine learning algorithms. This helps AI machines to interpret the world better. 

Be it enabling self-driving cars, or assisting with medical diagnostics, image annotation plays an important role. This blog is about various image annotation techniques and why selecting the right method is of utmost importance. 

What is Image Annotation

In the image annotation process, visual data are labeled to make it comprehensive for machine learning models. Annotators label objects, regions, or pixels in an image in order to create a dataset. 

This dataset trains algorithms to perform certain tasks. This includes object detection, image segmentation, and classification. On the basis of application, annotations can range from simple bounding boxes to complex pixel-level labeling.

Types of Image Annotations:

Common Types of image Annotations Are:

• Bounding Boxes:

Bounding boxes are rectangles drawn around objects of interest in an image. They are commonly used in object detection. The main objective is to find vehicles, pedestrians, or animals. Retail industries use bounding boxes to identify products on shelves. The e-commerce surveillance systems rely on them to detect suspicious activities. 

Although bounding boxes are easy to implement and interpret, they mostly fail when dealing with objects that have irregular shapes. This is because they cannot capture exact contours. Despite this limitation, their simplicity makes them popular for many detection tasks.

• Semantic Segmentation

In the semantic segmentation technique, a class label is assigned to each pixel in an image. It provides information about the shapes of objects. It is one of the most important techniques in autonomous driving. It can be used to identify roads, lanes, pedestrians, and vehicles.

In agriculture, it maps vegetation, crops, or soil types.  In medical imaging, it is used to delineate tumors, organs, or tissues. The pixel-level accuracy of this method makes it indispensable for tasks requiring spatial detail. A major demerit of semantic segmentation is that it demands significant computational resources. Also, it requires a lot of time for annotation.

• Instance Segmentation

Instance segmentation is an extended version of semantic segmentation. It works by differentiating between individual instances of the same class. For example, it can distinguish between multiple pedestrians or cars in autonomous driving scenarios. Robotics applications benefit from instance segmentation.

It is utilized for tasks like picking specific items in an unorganized environment. For wildlife monitoring, this method is used to track individual animals. Instance segmentation is a combination of object detection and semantic segmentation. Hence instance segmentation is ideal for applications that require identifying distinct objects. However, the scenario from which it should be detected is more complex and resource-consuming. 

• Polygon Annotation

Polygon annotation is the drawing of custom shapes around objects. It is perfect for irregularly shaped items. This method is widely used in geospatial analysis. To outline buildings, water bodies, or vegetation in satellite images, it is the best method. E-commerce platforms use polygon annotations to precisely label products with unusual shapes.

Wildlife researchers employ them to track animals with irregular forms. This technique is time-consuming and requires skilled annotators. But this method guarantees high precision. It is, therefore, suitable for tasks that require accuracy over speed.

• Keypoint And Landmark Annotation

Keypoint annotation marks particular points of interest within an image. Some examples are eyes, nose, mouth for facial recognition, and joints. The mouth is mainly for facial recognition while joints are for pose estimation. Keypoint annotation is widely applied in motion capture, where the movements of a human or an animal are followed. 

In the healthcare sector, this method is used for finding anatomical landmarks in X-rays or MRIs. The keypoint annotation can give a very fine-grained analysis of the features of objects. However, a major issue with it is that it only applies to cases where fine-grained structural information is required.

• 3D Cuboids

Three-dimensional cuboids expand the bounding boxes to three dimensions. They are objects defined in terms of length, width, and height. It is one of the fundamental aspects of autonomous driving. This method can be used to estimate the size and distance of a vehicle or obstacles. Similarly, in augmented reality, they place virtual objects in real-world environments. In robotics, they help in navigating complex spaces. The spatial context is added with 3D cuboids. Hence is done in a more complex manner. So annotating 3D cuboids requires special tools and expertise.

• Line And Polyline Annotation

Line annotation involves drawing straight or curved lines to capture paths, boundaries, or edges. A polyline annotation represents paths or trajectories. These annotations are critical in autonomous driving. This mainly helps in detecting lane boundaries and road edges. 

Other fields where this method is popular are cartography and infrastructure development.  In infrastructure management, it is used for mapping power lines or pipelines. In the field of cartography, line annotation is used for outlining trails or roads. Their simplicity and efficiency make them such a useful method for tasks involving linear objects. For this same reason, it is limited to a certain use only. Can be used in all situations.

How To Choose The Right Annotation Type

It is now clear that for each purpose, different annotation types can be used. A universal annotation is not available. Hence the choice of the annotation types depends on specific factors. They are:

• Application

Applications for each annotation type are different. Hence the specific requirement and its matching annotation type should be known beforehand. Sometimes the task may require precise object boundaries. Sometimes it may just need some local information.

• Complexity of Data

Certain data could be more complex hence simpler annotation types will be of no use. It should be specifically known whether the objects in the data are complex, overlapping, or irregularly shaped.

• Availability of Resources

Certain annotation types require a lot of time to interpret data. Some may be very costly. Hence the time availability and budget must be foreseen before selecting an annotation type.

Some examples are

• Bounding boxes are mainly used for general object detection.
• Semantic segmentation is used in applications like medical imaging due to its detailing.
• Irregular-shaped objects can be detected by Polygon annotation.

Tools For Image Annotation

Modern annotation tools have simplified the whole image annotation process. Additional features like AI-assisted labeling have completely changed the complexion of image annotation. Some of the popular tools used for it are:

• Labelbox

 It is a complete platform that includes bounding boxes, segmentation, and other annotation types.

• Superannotate

It provides a pixel-level annotation. Other project management features are also available on this software.

• CVAT

CVAT, also known as a Computer vision annotation tool, is open source. It can be easily customized.

All these tools improve accuracy and efficiency in creating quality datasets.

Conclusion:

Image annotation is an important step in developing computer vision systems.  This enables machines to interpret and interact with the world. Knowing the different types of annotations can guide you in choosing the most appropriate method for your project. 

This can vary from bounding boxes to semantic segmentation. Each annotation type plays a unique role in training models for diverse applications. It ranges from autonomous vehicles and medical imaging to retail and robotics.

With technological advancements, high-quality annotated data has become very important. The new tools and advanced techniques used are now more efficient and accurate in annotation processes. It greatly reduced the time to create a quality dataset. 

Ultimately, investment in the right annotation strategy is not about just improving model performance. The full potential of artificial intelligence should be utilized across industries. All the above annotation types can be exploited by businesses and researchers. In this way, intelligent systems capable of solving real-world problems can be created.