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

Anton Osokin, Dmitry Vetrov, Vladimir Kolmogorov
2011 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, submodular decomposition (SMD). Unlike existing decomposition approaches SMD decomposes the initial problem into subproblems corresponding to a specific class label while preserving the graph structure of each subproblem. Such
more » ... mposition enables us to take into account several types of global constraints in an efficient manner. We study theoretical properties of the proposed approach and demonstrate its applicability on a number of problems.
doi:10.1109/cvpr.2011.5995361 dblp:conf/cvpr/OsokinVK11 fatcat:ajjgbzyjhvbzji57xtcic2rqbi