Minimum fuzzy divergence based image cosegmentation

Multi-view cosegmentation for the same object is the basis of true three-dimensional imaging. Due to changes in the foreground and interferences from the background of the images, traditional cosegmentation algorithms often cannot fully and effectively extract common areas. To solve this problem, in this paper,we propose a new image cosegmentation algorithm which incorporates the minimum fuzzy divergence and active contours model.Considering the foreground similarity and background consistency between multiple images,the energy functions of images are generated. We lead color information covered by an image into the energy function of another image to enhance the robustness of curve evolution.Then we minimize the energy function value via the minimum fuzzy divergence. The experimental demonstrate that the proposed method can effectively segment the common objects from multi-view image pairs with generating lower error rates than that of traditional cosegmentation methods.