The endless repaint.

Image segmentation is a process of partitioning an image into multiple classes by assigning a class label to every pixel. Off-lately the advances in Deep Learning has made this to be a very easy task, especially when you have a good amount of annotated data for training a supervised image segmentation model. There are also other approaches that one can employ if there is no annotated data available and if you want to do a completely unsupervised image segmentation. Historically, clustering techniques and probabilistic graphical models (PGM) were used to perform image segmentation task, which I feel is still very relevant in a lot of scenarios where it is difficult to obtain training data. A clustering based image segmentation method will form groups of pixels according to their RGB values representing the color or other low-level properties which are called superpixels.  As part of this blog post we will see how to do background and foreground segmentation using K-Means. We will use th