Color Image Segmentation by Using Global Similarity Measure

A novel color image segmentation algorithm by using global similarity measure is proposed in this paper. The algorithm states the image segmentation problem as energy functional minimization. The Bhattacharyya distance is utilized to measure the global similarity of probability distribution functions between foreground and background. The Bhattacharyya coefficient is adopted as final energy functional. Since the highorder energy term is introduced, minimization of such energy is generally NPhard. In order to optimize the energy functional efficiently, an auxiliary upper bound function is proposed and it is optimized by graph cuts. The auxiliary function can guarantee to decrease the energy during the optimization process. The algorithm can be used in various segmentation problems, including interactive segmentation and sailency segmentation. Experimental results show that the proposed algorithm has global segmentation feature, which can segment color images correctly.