Kolmogorov, Vladimir
Vladimir
Kolmogorov
Schoenemann, Thomas
Thomas
Schoenemann
Generalized sequential tree-reweighted message passing
ArXiv
2012
2018-12-11T12:00:23Z
2020-05-12T08:50:48Z
preprint
https://research-explorer.app.ist.ac.at/record/2928
https://research-explorer.app.ist.ac.at/record/2928.json
1205.6352
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.