Structured learning of sum-of-submodular higher order energy functions

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 3104-3111

  Fix, Alexander ^a   Joachims, Thorsten ^a   Park, Sung Min ^a   Zabih, Ramin ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

Graph cuts; Max flow; Structured prediction

Indexed keywords

GRAPHIC METHODS; POLYNOMIAL APPROXIMATION; SYSTEMS ENGINEERING;

CUTTING PLANE ALGORITHMS; DISCRIMINATIVE LEARNING; EXPERIMENTAL COMPARISON; GRAPH CUT; INTERACTIVE SEGMENTATION; MAX FLOW; STRUCTURED LEARNING; STRUCTURED PREDICTION;

LEARNING SYSTEMS;

EID: 84898794807     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.385     Document Type: Conference Paper

References (35)

1. D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, and A. Y. Ng. Discriminative learning of Markov Random Fields for segmentation of 3D scan data. In CVPR, 2005.
2. C. Arora, S. Banerjee, P. Kalra, and S. N. Maheshwari. Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP. In ECCV, 2012.
3. M.-F. Balcan and N. J. A. Harvey. Learning submodular functions. In STOC, 2011.
4. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI, 26(9), 2004.
5. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. TPAMI, 23(11), 2001.
6. J. Edmonds and R. Giles. A min-max relation for submodular functions on graphs. Annals of Discrete Mathematics, 1:185-204, 1977.
7. T. Finley and T. Joachims. Training structural SVMs when exact inference is intractable. In ICML, 2008.
8. A. Fix, A. Gruber, E. Boros, and R. Zabih. A graph cut algorithm for higher-order Markov Random Fields. In ICCV, 2011.
9. V. Franc, B. Savchynskyy. Discriminative learning of maxsum classifiers. In JMLR, 2008.
10. S. Fujishige and X. Zhang. A push/relabel framework for submodular flows and its refinement for 0-1 submodular flows. Optimization, 38(2):133-154, 1996.
11. A. V. Goldberg, S. Hed, H. Kaplan, R. E. Tarjan, and R. F. Werneck. Maximum flows by incremental breadth-first search. In European Symposium on Algorithms, 2011.
12. V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman. Geodesic star convexity for interactive image segmentation. In CVPR, 2010.
13. X. He, R. S. Zemel, and M. A. Carreira-Perpiñ́an. Multiscale conditional random fields for image labeling. In CVPR, 2004.
14. H. Ishikawa. Transformation of general binary MRF minimization to the first order case. TPAMI, 33(6), 2010.
15. T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural SVMs. Machine Learning, 77(1), 2009.
16. P. Kohli, M. P. Kumar, and P. H. Torr. P3 and beyond: Move making algorithms for solving higher order functions. TPAMI, 31(9), 2008.
17. P. Kohli, L. Ladicky, and P. Torr. Robust higher order potentials for enforcing label consistency. IJCV, 82, 2009.
18. V. Kolmogorov. Minimizing a sum of submodular functions. Discrete Appl. Math., 160(15):2246-2258, Oct. 2012.
19. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? TPAMI, 26(2), 2004.
20. H. Koppula, A. Anand, T. Joachims, and A. Saxena. Semantic labeling of 3D point clouds for indoor scenes. In NIPS, 2011.
21. V. Kwatra, A. Schodl, I. Essa, G. Turk, and A. Bobick. Graphcut textures: Image and video synthesis using graph cuts. SIGGRAPH, 2003.
22. L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr. Associative hierarchical CRFs for object class image segmentation. In ICCV, 2009.
23. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
24. H. Lin and J. Bilmes. Learning mixtures of submodular shells with application to document summarization. In UAI, 2012.
25. D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert. Contextual classification with functional max-margin Markov networks. In CVPR, 2009.
26. J. B. Orlin. A faster strongly polynomial time algorithm for submodular function minimization. Math. Program., 118(2):237-251, Jan. 2009.
27. S, Roth and M, Black. Fields of experts. IJCV, 82, 2009.
28. C. Rother, V. Kolmogorov, and A. Blake. "GrabCut"-interactive foreground extraction using iterated graph cuts. SIGGRAPH, 23(3):309-314, 2004.
29. R. Sipos, P. Shivaswamy, and T. Joachims. Large-margin learning of submodular summarization models. In EACL, 2012.
30. M. Szummer, P. Kohli, and D. Hoiem. Learning CRFs using graph cuts. In ECCV, 2008.
31. B. Taskar, V. Chatalbashev, and D. Koller. Learning associative markov networks. In ICML, 2004.
32. B. Taskar, V. Chatalbashev, and D. Koller. Learning associative markov networks. In ICML. ACM, 2004.
33. B. Taskar, C. Guestrin, and D. Koller. Maximum-margin markov networks. In NIPS, 2003.
34. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004.
35. Y. Yue and T. Joachims. Predicting diverse subsets using structural SVMs. In ICML, 2008.