Unsupervised figure-ground segmentation using edge detection and game-theoretical graph-cut approach
Yu-Min Hsiao, Long-Wen Chang · 2015
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, those methods need some interactions of users. It makes those methods unfavorable. For example, Graph-Cut needs users to select a part of foreground and background to be foreground seeds and background seeds. A graph and min-cut theory is used to separate the foreground from the image. 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 into foreground and background. According to our experiment results, our method does not need user interaction and performs very well compared with the previous Graph-Cut method.