An integrated framework for image segmentation and perceptual grouping

This paper presents an efficient algorithm for image segmentation and a framework for perceptual grouping. It makes an attempt to provide one way of combining bottom-up and top-down approaches. In image segmentation, it generalizes the Swendsen-Wang cut algorithm (SWC) by Barbu and Zhu (2003) to make both 2-way and m-way cuts, and includes topology change processes (graph repartitioning and boundary diffusion). The method directly works at a low temperature without using annealing. We show that it is much faster than the DDMCMC approach (Tu and Zhu, 2002) and more robust than the SWC method. The results are demonstrated on the Berkeley data set. In perceptual grouping, it integrates discriminative model learning/computing, a belief propagation algorithm (BP) by Yedidia et al. (2000), and SWC into a three-layer computing framework. These methods are realized as different levels of approximation to an "ideal" generative model. We demonstrate the algorithm on the problem of human body configuration.