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Beyond Trees: MRF Inference via Outer-Planar Decomposition

9 years 3 months ago
Beyond Trees: MRF Inference via Outer-Planar Decomposition
Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. trees), or approximate algorithms (e.g. Loopy Belief Propagation (BP) and Tree-reweighted (TRW) methods). This paper presents a unifying perspective of these approximate techniques called "Decomposition Methods". These are methods that decompose the given problem over a graph into tractable subproblems over subgraphs and then employ message passing over these subgraphs to merge the solutions of the subproblems into a global solution. This provides a new way of thinking about BP and TRW as successive steps in a hierarchy of decomposition methods. Using this framework, we take a principled first step towards extending this hierarchy beyond trees. We leverage a new class of graphs amenable to exact inference, called outerplanar graphs, and propose an approximate inference algorithm called Outer-Planar D...
Dhruv Batra, Andrew Gallagher, Devi Parikh, Tsuhan
Added 01 Apr 2010
Updated 05 Apr 2011
Type Conference
Year 2010
Where CVPR
Authors Dhruv Batra, Andrew Gallagher, Devi Parikh, Tsuhan Chen
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