Generalized sequential treereweighted message passing
Abstract
This paper addresses the problem of approximate MAPMRF 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 TRWS 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 minsum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems.
 Publication:

arXiv eprints
 Pub Date:
 May 2012
 DOI:
 10.48550/arXiv.1205.6352
 arXiv:
 arXiv:1205.6352
 Bibcode:
 2012arXiv1205.6352K
 Keywords:

 Computer Science  Computer Vision and Pattern Recognition