Markov random fields with efficient approximations (1998)
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| Venue: | In IEEE Conference on Computer Vision and Pattern Recognition |
| Citations: | 123 - 21 self |
BibTeX
@INPROCEEDINGS{Boykov98markovrandom,
author = {Yuri Boykov and Olga Veksler and Ramin Zabih},
title = {Markov random fields with efficient approximations},
booktitle = {In IEEE Conference on Computer Vision and Pattern Recognition},
year = {1998},
pages = {648--655}
}
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Abstract
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. We also apply our techniques to MRF’s with linear clique potentials. 1
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