MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
By M. Pawan Kumar, Daphne Koller. 2009
39 citations recorded by the source corpus.
We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semi-metric pairwise potentials. For this problem, we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st-MINCUT problem. Unlike previous move making approaches, e.g. the widely used α-expansion algorithm, our method obtains the guarantees of the standard linear programming (LP) relaxation for the important special case of metric labeling. Unlike the existing LP relaxation solvers, e.g. interior-point algorithms or tree-reweighted message passing, our method is significantly faster as it uses only the efficient st-MINCUT algorithm in its design. Using both synthetic and real data experiments, we show that our technique outperforms several commonly used algorithms.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
- Yuri Boykov
- Olga Veksler
- Lena Gorelick
- Andrew Delong
- Alan L. Yuille
- Liang-Chieh Chen
- George Papandreou
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Alan L. Yuille
- Olga Veksler
- Dacheng Tao
- Jitendra Malik
- Li Fei-Fei
- Ming-Hsuan Yang
Citations in the corpus
- Minimizing Energies with Hierarchical Costs (2012, methodology): “5 is the same as that used by Kumar and Koller (2009).”