What is Image Annotation and its Types?

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Facial recognition, autonomous cars, and the internet of things are some of the examples where devices are now able to identify images automatically. However, artificial intelligence (AI) is as good as its training data. Incorrect training data can lead to the inaccurate or poor performance of your AI engine. Image annotation is an essential practice leveraged to develop training data for robust computer vision. Annotated images are required to enable machines to recognize objects in their surroundings and train Machine Learning (ML) algorithms to see the world exactly like humans do.

Image annotation in ML is basically the process through which labeling of data is done for images along with videos and texts. The predetermined labels are put by a computer vision scientist or machine learning engineer to deliver computer vision model info on the objects present in an image.

The ML algorithm then leverages the annotated images to learn and identify similar patterns when fed with a new set of data. As per the project requirement, different projects need different types of image annotation services.

The following types of image annotations are used to feed ML algorithms for image recognition.

Let us have a look.

Bounding Box

The bounding box is one of the most common and simple image annotation practices. It requires image annotation labelers to create a box around the edges of the required objects present within the picture. 2D bounding boxes are often used for object classification, localization, and detection by multiple industries including eCommerce, retail, healthcare, and more.

Polygon Annotation

All objects are not shaped in a regular size and might not fit appropriately in the bounding box. Polygon annotation is leveraged to ensure more accurate annotation for irregularly shaped items including non-symmetrical objects like trees, fruits, houses, landmarks, and more. It demands a greater level of precision from the labeler to achieve desired results.

Line Annotation

As the name goes, Line annotation is used to annotate splines and lines used to create boundaries in an image’s region. It is majorly used for a thin or small section where you can’t annotate the image with a bounding box efficiently. Unlike the bounding box, it helps in reducing the additional noise and white space present in the image. Line annotation is leveraged extensively to label images for autonomous cars and vehicles.

Point Annotation

Point annotation is done to achieve correct and reliable plotting of key points present at specific locations on the images. It is commonly used to support sentiment analysis or facial recognition algorithms. By recognizing and following the moves of marked points on a face’s expression, the ML algorithm can easily identify emotions by predictive reading.

Semantic Segmentation

Semantic segmentation is used to separate the images into several sections and classify each and every pixel in the segments with the related class label of what it reflects including car, lamp post, traffic light, pedestrian, and more. It ensures that the ML algorithm gets a robust understanding of each and every pixel of the image.

It is normally used to detect and localize specific objects in the image. The application of robust and granular intelligence to understand the images is commonly leveraged in multiple industries including autonomous vehicles. Self-driving vehicles need a deeper understanding of the surroundings to drive safely. In the agriculture industry, semantic segmentation is leveraged to analyze crop fields for the detection of abnormal growth and diseases.

In Summary

With an ever-evolving computer vision industry on a year on year basis, the methods to prepare training data is also evolving at a rapid pace. Having said that, image annotation is one of the most important tasks in achieving flawless computer vision efficiently.

Along with the right set of annotation tools, it is also crucial to make sure that the computer vision models rely on accurate and high quality annotated images. It is because it would translate into the accuracy of the ML algorithm and subsequently computer vision.

Hence it is crucial to partner with reliable and experienced image annotation services to ensure the availability of flawless training data in heaps. As we evolve technologically, more and more software and hardware will leverage AI and ML, and the requirement for image annotation is expected to grow at an exponential pace in the future.

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