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Understanding Image Annotation: How It Is Used To Build AI Models?

Image annotation is a crucial process in computer vision. It forms the base for many AI products that you interact with in your daily life. It uses metadata or tags, to identify data characteristics that you want your AI model to recognize.
Like humans, computers also need examples to learn to categorize things. With the increasing number of companies using AI, the demand for image annotation has also increased exponentially.

For companies within the machine learning area, it has become crucial to create a comprehensive and efficient image annotation process. Click here for more info related to this topic.

Types of Image Annotation

There are three types of image annotation, the details of which are mentioned below.

 1. Classification

This is the fastest method for image annotation and is mainly used for capturing abstract information. For example, it helps filter images that don’t meet the standards.

2. Object detection

In this method, annotators are required to label specific objects in an image. For example, if an image is classified as having orange in it, it will detect the location of the orange in the picture.

3. Semantic segmentation

This method is used to solve issues related to object detection overlapping. It ensures that every component of a picture belongs to a particular class. This is usually done at the pixel level.

Different Applications of Image Annotation

Image annotation is used in different fields. We have mentioned some of the common applications of the process.

1. Healthcare

Nowadays, hospitals have started implementing AI solutions to supplement their diagnoses. For example, AI is used for examining radiology images to detect the presence of certain types of cancers. AI is not intended to replace medical professionals. Rather it is used for making the right health decisions.

 2. Agriculture

The use of AI in agriculture provides plenty of benefits to farmers. Starting from evaluating soil conditions to estimating crop yield, it can perform various tasks that humans can’t. AI can annotate camera images to differentiate between crops and weeds at a pixel level. This information is used to determine the areas that need to be treated with the pesticide. This spares you from the pain of applying pesticides to the entire field.

 3. Finance

Yes, AI has penetrated the finance sector too. Compared to other industries, finance is lacking in terms of AI solutions. But there are companies like Caixabank that uses facial recognition feature to verify customers. This is made possible with the help of the pose-point. It is an image annotation process that scans facial features like the mouth and eyes. Image annotation is also used for annotating receipts.

4. Manufacturing

In the manufacturing sector, image annotation is used to record inventory information in warehouses. Manufacturers have installed training computers to evaluate image data. This helps you know when a product is out-of-stock. Some manufacturers use this technology to keep a check on their infrastructure.

Image annotation is typically a labor-intensive and time-consuming process. But training AI models to annotate images helps complete the task faster than humans.

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