Markov random fields with efficient approximations Export

@inproceedings{citeulike:1082732, abstract = {Markov Random Fields (MRFs) can be used for a wide variety of vision problems. In this paper we focus on MRFs with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph. We develop efficient algorithms for computing good approximations to the minimum multiway, cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRFs with linear clique potentials}, author = {Boykov, Y. and Veksler, O. and Zabih, R.}, citeulike-article-id = {1082732}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CVPR.1998.698673}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=698673}, doi = {10.1109/CVPR.1998.698673}, journal = {Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on}, keywords = {approximations, graphical-models, kikuchi-approximations, markov-network}, pages = {648--655}, posted-at = {2008-10-18 13:44:21}, priority = {2}, title = {Markov random fields with efficient approximations}, url = {http://dx.doi.org/10.1109/CVPR.1998.698673}, year = {1998} }

TY - CONF ID - citeulike:1082732 L3 - citeulike-article-id:1082732 N2 - Markov Random Fields (MRFs) can be used for a wide variety of vision problems. In this paper we focus on MRFs with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph. We develop efficient algorithms for computing good approximations to the minimum multiway, cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRFs with linear clique potentials JF - Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on EP - 655 TI - Markov random fields with efficient approximations SP - 648 KW - approximations KW - graphical-models KW - kikuchi-approximations KW - markov-network AU - Boykov, Y AU - Veksler, O AU - Zabih, R PY - 1998/// UR - http://dx.doi.org/10.1109/CVPR.1998.698673 ER -