Abstract
Image segmentation is an essential and challenging problem in computer vision and image processing. It categorized into two categories, multi-label segmentation and figure-ground segmentation. The goal of figure-ground segmentation is to separate the object from background. It can be used in object detection or many other applications. Recently, a lot of methods have been proposed for solving figure-ground segmentation problems. However, most of them are supervised approaches. In other words, the procedures of those methods need some interactions of users. It makes those methods unfavorable. For example, Graph-Cut needs user to select a part of foreground and background to be foreground seeds and background seeds. A graph and min-cut theory are used to separate the foreground from the image. Different from traditional Graph-Cut approaches, we proposed an unsupervised figure-ground approach. It uses an edge-based method to grab required information for Graph-Cut. Then, we use game-theoretical Graph-Cut to divide the image to foreground and background. According to our experiment results, our method does not need user interaction and performs well compared with the previous Graph-Cut method.