Efficient Exact Inference in Planar Ising Models

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Nicol Schraudolph, Dmitry Kamenetsky


We present polynomial-time algorithms for the exact computation of lowest- energy states, worst margin violators, partition functions, and marginals in binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence with perfect matchings in an expanded dual graph. Maximum-margin parameter estimation for a boundary detection task shows our approach to be efficient and effective.