Generalized Sequential Tree-Reweighted Message Passing
Thomas Schoenemann, Vladimir Kolmogorov · The MIT Press eBooks · 2014
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.