Abstract
Co-segmentation is a new topic in computer vision, which has been discussed lively in many literatures. It is defined as the task of jointly segmenting the common objects in a given set of images. Due to there are some limitations in previous methods, this thesis presents a game theoretic unsupervised approach by using the concept of heat diffusion and saliency to solve co-segmentation problem without these limitations. Our method is divided into two stages. First, the common objects discovery task is modeled by a cooperative game. In this game, each image is treated as player. All players want to maximize the overall payoffs (i.e. the gain of heat) by putting the heat sources appropriately. Note that we must ensure that no one will be likely to uncooperative. So we define some collaborative strategies. For each input image, the game structure generates corresponding labeled image which identifies the common objects and background. Then we use cooperative cut to solve energy minimization problem in the second stage. Our method takes advantage of cooperative game theory, which enables us to discover the common objects automatically and accurately. Experimental results demonstrate that in many cases the proposed method can perform much better than state-of-the-art co-segmentation method.