image annotation_computer vision

Image annotation as a subset of Computer Vision

Image annotation is one of the most important tasks in computer vision. Computer vision is considered as 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.

 

What is Image Annotation?

Image or data annotation, in simple words, is the task of annotating images with labels. 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 computer vision scientist or a machine learning engineer.

 

Types of image annotation

There are different techniques to annotate images, with each of the techniques having its 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.

 

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.

 

Semantic Segmentation

This is a pixel-wise annotation which 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.

 

3D cuboids

In 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.

 

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.

 

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.

 

Application of Image Annotation

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 from another and also objects.

The algorithms of face recognition are enhanced by image annotation techniques like key-point and landmark which frequently tracks 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 area and not restricted area using semantic segmentation, we can achieve this. Image annotation can also be used to detect suspicious activity.

 

E-Commerce

Image annotation is used to improve product listings and also helps in ensuring that the customers find the right products they are looking for. This is possible through semantic segmentation by tagging various components within search queries and product titles.

 

Robotics

One of the major applications of Image annotation is in Robotics. It helps robots in distinguishing between different types of objects and help 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 helps in restricting the robot in a particular area and not move out of the intended zone.

 

The growth of Computer Vision & the need for image annotation

According to Verified Market Research, 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. 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.

Over 90% of the time is spent preparing data sets for machine learning initiatives. For AI to evolve, machines are trained to identify and recognize visual data, and annotating the same can be a tedious task for those involved. Now that there are service providers who can create data sets from scratch for a machine learning or computer vision program, outsourcing the same is only wise.

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