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2013 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Miami, FL, USA
June 20, 2009 to June 25, 2009
ISBN: 978-1-4244-3992-8
pp: 2993-3000
H. Ishikawa , Dept. of Inf.&Biol. Sci., Nagoya City Univ., Nagoya, Japan
We introduce a new technique that can reduce any higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we combine the reduction with the fusion-move and QPBO algorithms to optimize higher-order multi-label problems. While many vision problems today are formulated as energy minimization problems, they have mostly been limited to using first-order energies, which consist of unary and pairwise clique potentials, with a few exceptions that consider triples. This is because of the lack of efficient algorithms to optimize energies with higher-order interactions. Our algorithm challenges this restriction that limits the representational power of the models, so that higher-order energies can be used to capture the rich statistics of natural scenes. To demonstrate the algorithm, we minimize a third-order energy, which allows clique potentials with up to four pixels, in an image restoration problem. The problem uses the fields of experts model, a learned spatial prior of natural images that has been used to test two belief propagation algorithms capable of optimizing higher-order energies. The results show that the algorithm exceeds the BP algorithms in both optimization performance and speed.
computer vision, higher-order clique reduction, binary graph cut, higher-order Markov random field, QPBO algorithm, higher-order multilabel problem, statistics, natural scene, third-order energy, image restoration, fields of experts model, natural image
H. Ishikawa, "Higher-order clique reduction in binary graph cut", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 00, no. , pp. 2993-3000, 2009, doi:10.1109/CVPRW.2009.5206689
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