Technology

Image annotation: Applications in Machine Learning and AI

Have you ever wondered how a camera detects objects?


For instance, during the pandemic, the government in many parts of the world installed cameras at the airport that can detect people without a face mask and alarm the airport authority. Similarly, phone cameras can detect a face, puppy, and other objects and tell you what the object is. How does a camera do that? Or for that matter, how does your phone camera unlocks your phone only on seeing your face and not others. 

A simple answer is artificial Intelligence (AI). A more thoughtful answer is image annotation. 

Let me explain. 

 Image annotation 

The first and foremost step in the development of AI models through machine learning ( ML) is obtaining a relevant training set. This training set helps algorithms understand the task at hand, see objects, and even predict outcomes in real life, making various tasks autonomous.  

 Visual perception –based AI models require images that contain objects that we see in real life. For the model to recognize objects in the images, the images have to be annotated.

Image annotation is the process of creating annotated images for AI models. Image annotation has substantial application in machine learning and artificial intelligence in terms of model success

 The basics of image annotation 

The purpose of image annotation is to help machines detect and recognize objects.  To do so, images are annotated with metadata for the description of the object. A huge amount of similar data (images) is feed into the model so that it becomes trained enough to recognize the objects when the model encounters a similar product in real-life situations. 

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 Applications of image annotation 

Annotated images are most widely used in machine learning and artificial intelligence to detect different types of objects. It has an equally substantial application in deep learning. Here are five prominent applications of image annotation. 

Detect an object of interest 


Object detection is perhaps the most common and widely used application of image annotation. In an image, particular objects need to be identified while the image can stay as it is. To make a machine recognize that one particular object, image annotation is used to annotate images. 

Recognize objects 

After object detection, object recognition is another application of image annotation. This helps machines categorize objects into human, non-living, or other desirable categories. 

Both object detection and recognition run simultaneously. During the annotation, metadata or notes are added to describe the attributes or nature of the object. This helps recognize the objects and store information for future references. 

Object classification 

A puppy in an image is different from a man with a puppy. Though both contain puppies. The two objects are entirely different. Image annotation helps in the classification. There are multiple techniques to annotate images and categorize objects that help visual perception AI models detect and categorize objects. 

Recognize human faces 

 Image annotation has a substantial application in facial recognition algorithms. The faces of humans are annotated from one point to another, measuring the dimension of the face and its various features like chin, ears, eyes, nose, and mouth. These facial markers are annotated and provided to the image classification system. Image annotation, thus, plays a significant role in human face recognition systems.  

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Conclusion 

Image annotation, overall, is a mix of sophisticated techniques to annotate images that trains AI models to detect, recognize, and classify objects. This has substantial applications in real life. Most AI-powered devices and autonomous objects that make things easier for us use image annotation to work the way they do. As an AI engineer, knowing image annotation and its various techniques could go a long way in pushing your career.   

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