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image annotation Maxicus

Image annotation is a significant task in computer vision. Computer vision is considered one of the most important fields of machine learning and AI development. 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.

The global computer vision market was valued at USD 13.75 billion in 2019 and is projected 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. This task is usually done by humans and is very time taking.

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 image 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.

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.

Bounding boxes

This is one of the most commonly used types of annotation. This type of image annotation is generally used in localization and object detection tasks. To define the location of the object, rectangular boxes are used. These are usually represented by the coordinates of the rectangular box. For instance, bounding boxes are frequently used in localization tasks and object detection.

bounding boxes


Polygonal segmentation

As all objects cannot be fit into a rectangular box due to their shape, complex polygons are used instead of rectangle boxes to define the shape and location of the target object in a much precise manner. This type of segmentation through complex polygons is called polygonal segmentation. This allows the capture of objects with an irregular shape.

polygonal segmentation

Semantic segmentation

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.

Semantic segmentation

3D cuboids

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.

Taking the same example of autonomous vehicles, 3D cuboids are used to find the distance between the car and any object in the surrounding environment.

3D cuboids

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.Key-point and Landmark

Line annotation

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.

Line annotation


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 the industries.

Face recognition

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 objects from another. The algorithms of face recognition are enhanced by image annotation techniques like key-point and landmarks which frequently track different points in different parts of the face by track pointing.

Agriculture technology

Image annotation techniques have been adopted in the agriculture-technology industry for various tasks. Detection of plant diseases by recognizing the images of both diseased and healthy crops can be done by using bounding boxes or semantic segmentation types. This is one of the most basic uses of image annotation in agriculture technology.

Security systems

Image annotation can be used in security systems to 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 can also be used to detect suspicious activity.


Image annotation is used to improve product listings and also helps in ensuring that the customers find the right products that they are looking 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. Also, line annotation can be used to help robots distinguish between different parts of a production line. 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. For AI to evolve, machines are trained to identify and recognize visual data, and 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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