Exact optimization for Markov random fields with convex priors
Ishikawa, H.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 25, Issue 10, Oct. 2003 Page(s): 1333 - 1336
Digital Object Identifier   10.1109/TPAMI.2003.1233908
Summary: We introduce a method to solve exactly a first order Markov random field optimization problem in more generality than was previously possible. The MRF has a prior term that is convex in terms of a linearly ordered label set. The method maps the problem into a minimum-cut problem for a directed graph, for which a globally optimal solution can be found in polynomial time. The convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method.

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