Generalized sequential tree-reweighted message passing

V. Kolmogorov, T. Schoenemann, ArXiv (2012).

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Abstract
This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems.
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Date Published
2012-05-29
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arXiv
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Cite this

Kolmogorov V, Schoenemann T. Generalized sequential tree-reweighted message passing. arXiv. 2012.
Kolmogorov, V., & Schoenemann, T. (2012). Generalized sequential tree-reweighted message passing. ArXiv. ArXiv.
Kolmogorov, Vladimir, and Thomas Schoenemann. “Generalized Sequential Tree-Reweighted Message Passing.” ArXiv. ArXiv, 2012.
V. Kolmogorov and T. Schoenemann, “Generalized sequential tree-reweighted message passing,” arXiv. ArXiv, 2012.
Kolmogorov V, Schoenemann T. 2012. Generalized sequential tree-reweighted message passing. arXiv.
Kolmogorov, Vladimir, and Thomas Schoenemann. “Generalized Sequential Tree-Reweighted Message Passing.” ArXiv, ArXiv, 2012.

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