Semantic Segmentation

What is semantic segmentation?

Semantic Segmentation is very useful for tasks that consist of image analysis. It is done by describing the process of linking each pixel of an image source with a class label (they can be named after objects like rocks, sky, etc.)

Applications are numerous for semantic segmentation which include Autonomous driving, medical surgeries, and land-area calculation to name a few.

There are various benefits of partitioning an image into multiple segments. The ultimate goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyse.

Dense Prediction

Each pixel of the image source must be labelled with a corresponding class of what it represents, this is the main goal for any semantic segmentation.

While going through the image, each and every pixel is labelled in a predictive manner and mass prediction at this scale is referred to as “dense prediction”, which saves time manifold.

With dense prediction, the ability to pick out objects inside a crowded image becomes much more easier and gives an advantage to computer vision, where the model handles a complex image.