Image segmentation is a Computer Vision technology that focuses on dividing the image into segments, which are subgroups of pixels representing some distinct categories. By grouping the similar regions in the image, it is easier to process it further or to divide it into separate images.
Segmentation essentially assigns a label to each pixel in the image and creates pixel-wise image masks. Each of these masks represents a certain object or shows parts of the image with the same characteristic. Image segmentation usually divides the image into several segments that cover it completely, with one of them often being the background, or in a set of boundaries representing each separate object.
Image segmentation can be placed into two categories: semantic segmentation and instance segmentation. Semantic segmentation focuses on detecting object classes on the image, so, for example, every cat on the image will be classified into the class “cats”.
On the other hand, instant segmentation finds the corresponding instance of the object, detecting all possible objects of such class on an image. In this case, every cat will have a separate individual label.
Why is it important?
The importance of image segmentation is in allowing computers to know where exactly each object is located. This knowledge will also help to calculate the spatial properties of the situation. Therefore, it already found crucial applications in self-driving cars, medical and satellite imaging, and other fields. For example, image segmentation is essentially the main technology that enabled the recent rapid progress in self-driving cars or cancer detection with AI.