MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation

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 non-optimal fixed points.

• Type: Conference
• Year: 2010
• Where: ECCV
• Keywords: Computer Vision, Discrete Graphical Models, ECCV 2010, Lagrangian Relaxation, Local Polytope Relaxation