An Image Segmentation Method for Chinese Paintings by Combining Deformable Models with Graph Cuts

In recent years researchers have developed many graph theory based algorithms for image setmentation. However, previous approaches usually require trimaps as input, or consume intolerably long time to get the final results, and most of them just consider the color information. In this paper we proposed a fast object extraction method. First it combines deformable models information with explicit edge information in a graph cuts optimization framework. we segment the input image roughly into two regions: foreground and background. After that, we estimate the opacity values for the pixels nearby the foreground/ background border using belief propagation (BP). Third, we introduce the texture information by building TCP images' co-occurrence matrices. Experiments show that our method is efficient especially for TCP images.