Beyond trees: MRF inference via outer-planar decomposition

  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},
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
Highly Cited
This paper has 35 citations. REVIEW CITATIONS
25 Citations
39 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 25 extracted citations


Publications referenced by this paper.
Showing 1-10 of 39 references

An annotated bibliography on the thickness, outerthickness, and arboricity of a graph

  • E. Mäkinen, T. Poranen
  • Technical report, University of Tampere,
  • 2009
1 Excerpt

Dynamic planar-cuts: Efficient computation of min-marginals for outer-planar models

  • D. Batra, T. Chen
  • In NIPS Workshop on Discrete Optimization in…
  • 2009
1 Excerpt

Similar Papers

Loading similar papers…