Generalized sequential tree-reweighted message passing
Kolmogorov, Vladimir
Schoenemann, Thomas
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.
ArXiv
2012
info:eu-repo/semantics/preprint
doc-type:preprint
text
http://purl.org/coar/resource_type/c_816b
https://research-explorer.ista.ac.at/record/2928
Kolmogorov V, Schoenemann T. Generalized sequential tree-reweighted message passing. <i>arXiv</i>. 2012.
eng
info:eu-repo/semantics/altIdentifier/arxiv/1205.6352
info:eu-repo/semantics/openAccess