MRF inference by k-fan decomposition and tight Lagrangian relaxation

Authors:
Jörg Hendrik Kappes;Stefan Schmidt;Christoph Schnörr
Affiliations:
HCI, University of Heidelberg, Germany;HCI, University of Heidelberg, Germany;HCI, University of Heidelberg, Germany
Venue:
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Year:
2010

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Abstract

We present a novel dual decomposition approach to MAP inference with highly connected discrete graphical models. Decompositions into cyclic k-fan structured subproblems are shown to significantly tighten the Lagrangian relaxation relative to the standard local polytope relaxation, while enabling efficient integer programming for solving the subproblems. Additionally, we introduce modified update rules for maximizing the dual function that avoid oscillations and converge faster to an optimum of the relaxed problem, and never get stuck in nonoptimal fixed points.