Markov Random Fields with Efficient Approximations

Yuri Boykov, Olga Veksler, Ramin Zabih

In IEEE conference on Computer Vision and Pattern Recognition (CVPR) , pp. 648-655, 1998.


Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's 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 (visit our image gallery). We also apply our techniques to MRF's with linear clique potentials.

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