Generalized sequential tree-reweighted message passing
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.
Cite
@article{arxiv.1205.6352,
title = {Generalized sequential tree-reweighted message passing},
author = {Vladimir Kolmogorov and Thomas Schoenemann},
journal= {arXiv preprint arXiv:1205.6352},
year = {2015}
}