What Are The Applications of Image Annotation in Machine Learning and AI?

What Are The Applications of Image Annotation in Machine Learning and AI? At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.

In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images.

The applications of image annotation in machine learning and AI is substantial in terms of model success.

What is Image Annotation?

So, right here we will discuss about the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.

And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.

5 APPLICATIONS OF IMAGE ANNOTATION Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.

Detect the Object of Interest The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.

Also Read: What Is Computer Vision: How It Works in Machine Learning and AI

Recognize the Types of Objects After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other manmade objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.

Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.

Classifying the Different Objects It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.

Also Read: What is the Importance of Image Annotation in AI And Machine Learning Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects.

Object Segmentation of Single Class Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.

Also Read: What is Semantic Image Segmentation and Types for Deep Learning The main applications of image annotation is to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.

Also Read: How To Label Data For Semantic Segmentation Deep Learning Models

Recognizing the Humans Faces AI cameras in smartphones or security surveillance, are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans faces recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.

In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system.

Hence, image annotation is playing another important role in recognizing the people from their faces.

Also Read: How To Select Suitable Machine Learning Algorithm For A Problem Statement

TYPES OF IMAGE ANNOTATION

I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.

Also Read: What are the various Types of Data Sets used in Machine Learning

Bounding Box Annotation to Easily Detect the Objects Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision.

All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.

Semantic Segmentation to Localize Objects in Single Class To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.

Also Read: How To Improve Machine Learning Model Performance: Five Ways

3D Point Cloud Annotation to Detect the Minor Objects The image annotation applications not only include object detection or recognition, even can also measure or estimate the

types and dimensions of the object. 3D point cloud annotation is the technique helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets is created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.

Landmark Annotation to Detect Human Faces & Gestures Landmark annotation is another type of image annotation techniques used to detect human faces. The AI models like AI cameras in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.

3D Cuboid Annotation to Detect the Object with Dimension Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects.

The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous

vehicles, AI robots and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.

Polygon Annotation to Detect Asymmetrical Shaped Objects Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.

Polyline/Splines/Line Annotation for Lane or Path Detection Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider about the divisions between important regions of an image. The boundaries, annotating lines or splines is useful to detect lanes for selfdriving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.

The right applications of image annotation is possible when you use the right tools and techniques to create high-quality training data sets for machine learning. And Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, Cogito can produce the machine learning training data sets for healthcare, agriculture, retail, automotive, drones and robotics.