Submodular decomposition framework for inference in associative Markov networks with global constraints

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 1889-1896

  Osokin, Anton ^a   Vetrov, Dmitry ^a   Kolmogorov, Vladimir ^b  

^a MOSCOW STATE UNIVERSITY   (Russian Federation)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; PATTERN RECOGNITION;

DECOMPOSITION APPROACH; GLOBAL CONSTRAINTS; GRAPH STRUCTURES; INITIAL PROBLEM; MARKOV NETWORKS; MARKOV RANDOM FIELDS; PRACTICAL IMPORTANCE; SPECIFIC CLASS;

STRUCTURAL FRAMES;

EID: 80052907427     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995361     Document Type: Conference Paper

References (32)

1. D. Batra, A. C. Gallagher, D. Parikh, and T. Chen. Beyond trees: MRF inference via outer-planar decomposition. In CVPR, 2010.
2. D. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.
3. Y. Boykov and G. Funka-Lea. Graph Cuts and Efficient N-D Image Segmentation. IJCV, 70(2):109-131, 2006.
4. Y. Boykov and V. Kolmogorov. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. PAMI, 26(9):1124-1137, 2004.
5. Y. Boykov, O. Veksler, and R. Zabih. Fast Approximate Energy Minimization via Graph Cuts. PAMI, 23(11):1222-1239, 2001.
6. J. Darbon. Global optimization for first order Markov random fields with submodular priors. Discrete Applied Mathematics, 157(16):3412-3423, 2009.
7. D. Freedman and T. Zhang. Interactive Graph Cut Based Segmentation With Shape Priors. In CVPR, 2005.
8. D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, Series B, 51(2):271-279, 1989.
9. H. Ishikawa. Exact Optimization for Markov Random Fields with Convex Priors. PAMI, 25(10):1333-1336, 2003.
10. J. M. Kleinberg and É. Tardos. Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields. J. ACM, 49(5):616-639, 2002.
11. P. Kohli and P. Torr. Measuring Uncertainty in Graph Cut Solutions. In ECCV, 2006.
12. P. Kohli and P. Torr. Dynamic Graph Cuts for Efficient Inference in Markov Random Fields. PAMI, 29(12):2079-1088, 2007.
13. V. Kolmogorov. Convergent Tree-Reweighted Message Passing for Energy Minimization. PAMI, 28(10):1568-1583, 2006.
14. V. Kolmogorov and M. Wainwright. On the Optimality of Tree-reweighted Max-product Message Passing. In UAI, 2005.
15. V. Kolmogorov and R. Zabih. What Energy Functions Can Be Optimized via Graph Cuts. PAMI, 26(2):147-159, 2004.
16. N. Komodakis and N. Paragios. Beyond loose LP-relaxations: Optimizing MRFs by repairing cycles. In ECCV, 2008.
17. N. Komodakis, N. Paragios, and G. Tziritas. MRF Energy Minimization and Beyond via Dual Decomposition. PAMI, 33(3):531-552, 2010.
18. N. Komodakis and G. Tziritas. Approximate Labeling via Graph-Cuts Based on Linear Programming. PAMI, 29(8):1436-1453, 2007.
19. S. L. Lauritzen. Graphical models. Clarendon Press, 1996.
20. Y. Lim, K. Jung, and P. Kohli. Energy Minimization Under Constraints on Label Counts. In ECCV, 2010.
21. A. Osokin, D. Vetrov, and V. Kolmogorov. Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints. Technical Report 1103.1077, arXiv, 2011.
22. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Francisco, 1988.
23. P. Ravikumar and J. Lafferty. Quadratic Programming Relaxations for Metric Labeling and Markov Random Fields MAP Estimation. In ICML, 2006.
24. N. N. Schraudolph and D. Kamenetsky. Efficient Exact Inference in Planar Ising Models. In NIPS, 2008.
25. D. Sontag, A. Globerson, and T. Jaakkola. Introduction to dual decomposition for inference. In S. Sra, S. Nowozin, and S. J. Wright, editors, Optimization for Machine Learning. MIT Press, 2011.
26. D. Sontag, T. Meltzer, A. Globerson, Y. Weiss, and T. Jaakkola. Tightening LP relaxations for MAP using message-passing. In UAI, 2008.
27. B. Taskar, V. Chatalbashev, and D. Koller. Learning Associative Markov Networks. In ICML, 2004.
28. O. Veksler. Star Shape Prior for Graph-Cut Image Segmentation. In ECCV, 2008.
29. M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky. MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory, 51(11):3697-3717, 2005.
30. T. Werner. A Linear Programming Approach to Max-sum Problem: A Review. PAMI, 29(7):1165-1179, 2007.
31. T. Werner. High-arity Interactions, Polyhedral Relaxations, and Cutting Plane Algorithm for Soft Constraint Optimisation (MAP-MRF). In CVPR, 2008.
32. O. J. Woodford, C. Rother, and V. Kolmogorov. A Global Perspective on MAP Inference for Low-Level Vision. In ICCV, 2009.