Submodular decomposition framework for inference in associative Markov networks with global constraints

  title={Submodular decomposition framework for inference in associative Markov networks with global constraints},
  author={A. Osokin and D. Vetrov and V. Kolmogorov},
  journal={CVPR 2011},
  • A. Osokin, D. Vetrov, V. Kolmogorov
  • Published 2011
  • Computer Science, Mathematics
  • CVPR 2011
  • In this paper we address the problem of finding the most probable state of discrete Markov random field (MRF) with associative pairwise terms. Although of practical importance, this problem is known to be NP-hard in general. We propose a new type of MRF decomposition, submod-ular decomposition (SMD). Unlike existing decomposition approaches SMD decomposes the initial problem into sub-problems corresponding to a specific class label while preserving the graph structure of each subproblem. Such… CONTINUE READING

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