Distributed message passing for large scale graphical models

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

Volumn , Issue , 2011, Pages 1833-1840

  Schwing, Alexander ^a   Hazan, Tamir ^b   Pollefeys, Marc ^a   Urtasun, Raquel ^b  

^a ETH ZURICH   (Switzerland)

^b TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CODES; GRAPHIC METHODS; INFERENCE ENGINES; PATTERN RECOGNITION; STEREO IMAGE PROCESSING;

GRAPHICAL MODEL; HIGH RESOLUTION IMAGERY; LARGE PROBLEMS; MEMORY REQUIREMENTS; MESSAGE PASSING ALGORITHM; OPTIMALITY; PARALLELIZING; STEREO RECONSTRUCTION;

MESSAGE PASSING;

EID: 80052908754     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995642     Document Type: Conference Paper

References (30)

1. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 2001. 1833, 1834
2. P. Felzenszwalb and D. Huttenlocher. Efficient belief propagation for early vision. IJCV, 70(1):41-54, 2006. 1833, 1834, 1838
3. W. T. Freeman, T. R. Jones, and E. C. Pasztor. Examplebased super-resolution. IEEE Comput. Graph. Appl., 22(2):56-65, 2002. 1833
4. A. Globerson and T. S. Jaakkola. Fixing max-product: convergent message passing algorithms for MAP relaxations. In NIPS, 2007. 1833, 1836
5. J. E. Gonzalez, Y. Low, and C. Guestrin. Residual splash for optimally parallelizing belief propagation. In AISTATS, pages 177-184, 2009. 1838
6. T. Hazan and A. Shashua. Convergent message-passing algorithms for inference over general graphs with convex free energies. In Conference on Uncertainty in Artifical Intelligence (UAI), Helsinki, Finland, July 2008. 1834
7. T. Hazan and A. Shashua. Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference. Arxiv preprint arXiv:0903.3127, 2009. 1833, 1834
8. T. Heskes. Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies. Journal of Artificial Intelligence Research, 26(1):153-190, 2006. 1833, 1836
9. P. Kohli, L. Ladickỳ, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. IJCV, 82(3):302-324, 2009. 1834
10. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? PAMI, pages 147-159, 2004. 1834
11. N. Komodakis, N. Paragios, and G. Tziritas. MRF Energy Minimization & Beyond via Dual Decomposition. PAMI, 2010. 1833, 1836
12. F. Kschischang, B. Frey, and H. Loeliger. Factor graphs and the sum-product algorithm. Trans. on Information Theory, 47(2):498-519, 2001. 1835
13. X. Lan, S. Roth, D. Huttenlocher, and M. Black. Efficient belief propagation with learned higher-order Markov random fields. In ECCV, pages 269-282, 2006. 1833
14. V. Lempitsky, C. Rother, and A. Blake. Logcut-efficient graph cut optimization for Markov random fields. In ICCV, pages 1-8, 2007. 1834
15. A. Levin and Y. Weiss. Learning to Combine Bottom-Up and Top-Down Segmentation. In ECCV, 2006. 1833
16. A. Levin, A. Zomet, and Y. Weiss. Learning how to inpaint from global image statistics. In ICCV, 2003. 1833
17. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. Hellerstein. Graphlab: A new parallel framework for machine learning. In UAI, 2010. 1834, 1837, 1838
18. J. Mooij. libDAI: A free/open source C++ library for discrete approximate inference methods, 2009. http://www.libdai.org. 1837
19. J. Mooij and H. Kappen. Sufficient conditions for convergence of loopy belief propagation. In UAI, 2005. 1834
20. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, 1988. 1833, 1834
21. Y. Pritch, E. Kav-Venaki, and S. Peleg. Shift-map image editing. In ICCV, pages 151-158, 2010. 1834
22. R. Rockafellar. Convex analysis. Princeton university press, 1970. 1835
23. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1/2/3):7-42, 2002. 1837
24. S. Shimony. Finding MAPs for belief networks is NP-hard. Artificial Intelligence, 68(2):399-410, 1994. 1835
25. P. Strandmark and F. Kahl. Parallel and distributed graph cuts by dual decomposition. In CVPR, 2010. 1834
26. J. Sun, N. Zheng, and H. Shum. Stereo matching using belief propagation. PAMI, pages 787-800, 2003. 1833
27. M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky. A new class of upper bounds on the log partition function. Trans. on Information Theory, 51(7):2313-2335, 2005. 1836
28. M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky. MAP estimation via agreement on trees: message-passing and linear programming. Trans. on Information Theory, 51(11):3697-3717, 2005. 1836
29. T. Werner. A linear programming approach to max-sum problem: A review. PAMI, 29(7):1165-1179, 2007. 1833, 1836
30. C. Yanover, T. Meltzer, and Y. Weiss. Linear Programming Relaxations and Belief Propagation-An Empirical Study. JMLR, 7:1907, 2006. 1835