Beyond trees: MRF inference via outer-planar decomposition

@article{Batra2010BeyondTM,
  title={Beyond trees: MRF inference via outer-planar decomposition},
  author={Dhruv Batra and Andrew C. Gallagher and Devi Parikh and Tsuhan Chen},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year={2010},
  pages={2496-2503}
}
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… CONTINUE READING
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