Graph Partition by Swendsen-Wang Cuts

Vision tasks, such as segmentation, grouping, recognition, can be formulated as graph partition problems. The recent literature witnessed two popular graph cut algorithms: the Ncut using spectral graph analysis and the minimum-cut using the maximum flow algorithm. This paper presents a third major approach by generalizing the Swendsen-Wang method? a well celebrated algorithm in statistical mechanics. Our algorithm simulates ergodic, reversible Markov chain jumps in the space of graph partitions to sample a posterior probability. At each step, the algorithm splits, merges, or re-groups a sizable subgraph, and achieves fast mixing at low temperature enabling a fast annealing procedure. Experiments show it converges in 230 seconds in a PC for image segmentation. This is 400 times faster than the single-site update Gibbs sampler, and 20-40 times faster than the DDMCMC algorithm. The algorithm can optimize over the number of models and works for general forms of posterior probabilities, so...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Adrian Barbu, Song Chun Zhu
• Keywords: Computer Vision, Graph Cut Approaches, Graph Partition Problems, ICCV 2003, Maximum Flow Algorithm, Popular Graph Cut, Spectral Graph Analysis