Global connectivity potentials for random field models

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 818-825

  Nowozin, Sebastian ^a   Lampert, Christoph H ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; INFERENCE ENGINES; MAGNETORHEOLOGICAL FLUIDS; MARKOV PROCESSES; STRUCTURAL FRAMES;

CUTTING PLANE ALGORITHMS; GLOBAL CONNECTIVITY; MARKOV RANDOM FIELDS; OBJECT SEGMENTATION; POLYHEDRAL COMBINATORICS; POTENTIAL FUNCTION; RANDOM FIELD MODEL; SEGMENTATION ERROR;

COMPUTER VISION;

EID: 70450184098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206567     Document Type: Conference Paper

References (38)

1. H. Bay, A. Ess, T. Tuytelaars, and L. J. V. Gool. Speeded-up robust features (SURF). CVIU, 110(3):346-359, 2008.
2. D. Bertsimas and J. N. Tsitsiklis. Introduction to Linear Optimization. 1997.
3. M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In ECCV, 2008.
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. P. Das, O. Veksler, V. Zavadsky, and Y. Boykov. Semiautomatic segmentation with compact shape prior. Image and Vision Computing, 2008.
6. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2008 Results. http://www. pascalnetwork.org/challenges/VOC/voc2008/workshop/.
7. T. Finley and T. Joachims. Training structural SVMs when exact inference is intractable. In ICML, 2008.
8. A. Globerson and T. Jaakkola. Fixing max-product: Convergent message passing algorithms for map lp-relaxations. In NIPS, 2007.
9. T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel. Discovering regulatory and signalling circuits in molecular interaction networks. In ISMB, 2002.
10. R. M. Karp. Maximum-weight connected subgraph problem, 2002. http://www.cytoscape.org/ISMB2002/.
11. P. Kohli, M. P. Kumar, and P. Torr. P3 & beyond: Solving energies with higher order cliques. In CVPR, 2007.
12. P. Kohli, L. Ladický, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. In CVPR, 2008.
13. P. Kohli, A. Shekhovtsov, C. Rother, V. Kolmogorov, and P. H. S. Torr. On partial optimality in multi-label MRFs. In ICML, volume 307, pages 480-487, 2008.
14. V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. PAMI, 28(10):1568-1583, 2006.
15. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? PAMI, 26(2):147-159, 2004.
16. N. Komodakis and N. Paragios. Beyond loose LPrelaxations: Optimizing MRFs by repairing cycles. In ECCV, 2008.
17. N. Komodakis, N. Paragios, and G. Tziritas. MRF optimization via dual decomposition: Message-passing revisited. In ICCV. IEEE, 2007.
18. M. P. Kumar, V. Kolmogorov, and P. Torr. An analysis of convex relaxations for MAP estimation. In NIPS, 2008.
19. M. P. Kumar and P. Torr. Efficiently solving convex relaxations for MAP estimation. In ICML, 2008.
20. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
21. Y. Li and D. Huttenlocher. Learning for stereo vision using the structured support vector machine. In CVPR, 2008.
22. G. Mori. Guiding model search using segmentation. In ICCV, 2005.
23. S. Ramalingam, P. Kohli, K. Alahari, and P. Torr. Exact inference in multi-label CRFs with higher order cliques. In CVPR, 2008.
24. X. Ren and J. Malik. Learning a classification model for segmentation. In ICCV, 2003.
25. A. Schrijver. Theory of Linear and Integer Programming. John Wiley & Sons, New York, 1998.
26. D. Sontag and T. Jaakkola. New outer bounds on the marginal polytope. In NIPS, 2007.
27. D. Sontag, T. Meltzer, A. Globerson, T. Jaakkola, and Y.Weiss. Tightening LP relaxations for MAP using message passing. In UAI, 2008.
28. C. Sutton and A. McCallum. An introduction to conditional random fields for relational learning. In Introduction to Statistical Relational Learning, chapter 4. 2007.
29. M. Szummer, P. Kohli, and D. Hoiem. Learning CRFs using graph cuts. In ECCV, 2008.
30. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 6:1453-1484, Sept. 2005.
31. S. Vicente, V. Kolmogorov, and C. Rother. Graph cut based image segmentation with connectivity priors. In CVPR, 2008.
32. M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky. MAP estimation via agreement on (hyper)trees: Message-passing and linear-programming approaches. IEEE Trans. Information Theory, 51(11):3697-3717, Nov. 2005.
33. Y.Weiss, C. Yanover, and T. Meltzer. Map estimation, linear programming and belief propagation with convex free energies. In UAI, 2007.
34. T. Werner. A linear programming approach to max-sum problem: A review. Research report, Center for Machine Perception, Czech Technical University, Dec. 2005.
35. T. Werner. High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF). In CVPR, 2008.
36. L. A. Wolsey. Integer Programming. John Wiley & Sons, New York, 1998.
37. C. Yanover, T. Meltzer, and Y. Weiss. Linear programming relaxations and belief propagation - an empirical study. JMLR, 7:1887-1907, 2006.
38. Y. Zeng, D. Samaras, W. Chen, and Q. Peng. Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in n-d images. CVIU, (112):81-90, 2008.