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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. Suchdoi:10.1109/cvpr.2011.5995361 dblp:conf/cvpr/OsokinVK11 fatcat:ajjgbzyjhvbzji57xtcic2rqbi