A comparative study of energy minimization methods for Markov random fields with smoothness-based priors

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 30, Issue 6, 2008, Pages 1068-1080

  Szeliski, Richard ^a   Zabih, Ramin ^b   Scharstein, Daniel ^c   Veksler, Olga ^d   Kolmogorov, Vladimir ^e   Agarwala, Aseem ^f   Tappen, Marshall ^g   Rother, Carsten ^h  

^a MICROSOFT RESEARCH   (United States)

^b School of Operations Research and Information Engineering   (United States)

^c MIDDLEBURY COLLEGE   (United States)

^d UNIVERSITY OF WESTERN ONTARIO   (Canada)

^e UNIVERSITY COLLEGE LONDON   (United Kingdom)

^f ADOBE RESEARCH   (United States)

^g University of Central Florida   (United States)

^h MICROSOFT RESEARCH   (United Kingdom)

Author keywords

Belief propagation; Global optimization; Graph cuts; Markov random fields; Performance evaluation

Indexed keywords

ALGORITHMS; ENERGY EFFICIENCY; GLOBAL OPTIMIZATION; GRAPH THEORY; IMAGE ANALYSIS; MARKOV PROCESSES;

ENERGY MINIMIZATION METHODS; GRAPH CUTS; MARKOV RANDOM FIELDS;

PIXELS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; IMAGE ENHANCEMENT; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 43249091850     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2007.70844     Document Type: Article

References (50)

1. A. Agarwala, M. Dontcheva, M. Agrawala, S. Drucker, A. Colburn, B. Curless, D. Salesin, and M. Cohen, "Interactive Digital Photomontage," ACM Trans. Graphics, vol. 23, no. 3 pp. 294-302, 2004.
2. A. Agarwala, C. Zheng, Pal, M. Agrawala, M. Cohen, B. Curless, D. Salesin, and R. Szeliski, "Panoramic Video Textures," ACM Trans. Graphics, vol. 24, no. 3, pp. 821-827, 2005.
3. R. Ahuja, Ö. Ergun, J. Orlin, and A. Punnen, "A Survey of Very Large-Scale Neigliborhood Search Techniques," Discrete Applied Math. vol. 123, nos. 1-3, pp. 75-102, 2002.
4. S. Barnard, "Stochastic Stereo Matching over Scale," Int'l J. Computer Vision, vol 3, no. 1, pp. 17-32, 1989.
5. J. Besag, "On the Statistical Analysis of Dirty Pictures (with discussion)," J. Royal Statistical Soc., Series B, vol. 48, no. 3, pp. 259-302, 1986.
6. S. Birchfield and C. Tomasi, "A Pixel Dissimilarity Measure That is insensitive to Image Sampling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 401-406, Apr. 1998.
7. A. Blake and A. Zisserman, Visual Reconstruction. MIT Press, 1987.
8. Y. Boykov and M.-P. Jolly, "Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images," Proc. Eighth IEEE Int'l Conf. Computer Vision, vol. 1, pp. 105-112, 2001.
9. Y. Boykov and V. Kolmogorov, "An Experimental Comparison of Min-Cut/ Max-Flow Algorithms for Energy Minimization in Vision," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124-1137, Sept. 2004.
10. Y. Boykov, O. Veksier, and R. Zabih, "Markov Random Fields with Efficient Approximations," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 648-655, 1998.
11. Y. Boykov, O. Veksler, and R. Zabih, "Fast Approximate Energy Minimization via Graph Cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
12. C. Buehler, S. Gortler, M. Cohen, and L. McMillan, "Minimal Surfaces for Stereo Vision," Proc. Seventh European Conf. Computer Vision, vol. 3, pp. 885-899, 2002.
13. P. Chou and C. Brown, "The Theory and Practice of Bayesian Image Labeling," Int'l J. Computer Vision, vol. 4, no. 3, pp. 185-210, 1990.
14. K. Driesen and U. Hölzle, " The Direct Cost of Virtual Function Calls in C++," Proc. ACM SIGPLAN Conf. Object-Oriented Programming Systems, Languages, and Applications, pp. 306-323, 1996.
15. A. Efros and W. Freeman, "Image Quilting for Texture Synthesis and Transfer," Proc. ACM SIGGRAPH '01, pp. 341-346, 2001.
16. P. Felzenszwalb nnd D. Huttenlocher, "Efficient Belief Propagation for Early Vision," Int'l J. Computer Vision, vol. 70, no. 1, pp. 41-54, 2006.
17. W. Freeman, E. Pasztor, and O. Carmichael, "Learning Low-Level Vision," Int'l J. Computer Vision, vol. 40, no. 1, pp. 25-47, 2000.
18. B. Frey and D. MacKay, "A Revolution: Belief Propagation in Graphs with Cycles," Advances in Neural Infomation Processing Systems, 1997
19. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
20. S. Geman and C. Graffigne, "Markov Randorn Field Image Models and Their Applications to Computer Vision," Proc. Int'l Congress of Math., pp. 1496-1517, 1986.
21. D Greig, B. Porteous, and A. Seheult, "Exact Maximum A Posteriori Estimation for Binary Images," J. Royal Statistical Soc., Series B vol. 51, no. 2, pp. 271-279, 1989.
22. P. Hammer, "Some Network Flow Problems Solved with Pseudo-Boolean Programming," Operations Research, vol. 13, pp. 388-399, 1965.
23. D. Hochbaum, "An Efficient Algorithm for Image Segmentation, Markov Random Fields and Related Problems," J. ACM, vol. 48, no. 4, pp. 686-701, 2001.
24. H. Ishikawa, "Exact Optimization for Markov Random Fields with Convex Priors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1333-1336, Oct. 2003.
25. P. Kohli and P. Torr, "Dynamic Graph Cuts for Efficient Inference in Markov Random Fields," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2079-2088, Dec. 2007.
26. V. Kolmogorov, "Convergent Tree-Reweighted Message Passing for Energy Minimization," IEEE Trans. Pattern Analysis and Machine Intelligence vol. 28, no. 10, pp. 1568-1583, Oct. 2006.
27. V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, nnd C. Rother, "Probabilistic Fusion or Stereo with Color and Contrast for Bilayer Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence vol. 28, no. 9, pp. 1480-1492, Sept. 2006.
28. V. Kolmogorov and C. Rother, "Comparison of Energy Minimization Algorithms for Highly Connected Graphs," Proc. Ninth European Conf. Computer Vision, pp. 1-15, 2006.
29. V. Kolmogorov and C. Rother, "Minimizing Nonsubmodular Functions with Graph Cuts - A Review," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 7. pp. 1274-1279, 2007.
30. V. Kolmogorov and R. Zabih, "Multi-Camera Scene Reconstruction vin Graph Cuts," Proc. Seventh European Conf. Computer Vision, pp. 82-96, 2002.
31. V. Kolmogorov and R. Zabih, "What Energy Functions can be Minimized via Graph Cuts," IEEE Trans. Pattern Analysis and Machine Intelligence vol. 26, no. 2, pp. 147-159, Feb. 2004.
32. N. Komodakis and G. Tziritas, "Approximate Labeling via Graph Cuts Based on Linear Programming," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8. pp. 1436-1453, Aug. 2007.
33. N. Komodakis, G. Tziritas, and N. Paragios, "Fast, Approximately Optimal Solutions for Single and Dynamic MRFs," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, June 2007.
34. V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, "Graphcut Textures: Image and Video Synthesis Using Graph Cuts," ACM Trans. Graphics, pp. 277-286, 2003.
35. S. Li. Markov Random Field Modeling in Computer Vision. Springer, 1995.
36. M. Lin and C. Tomasi, "Surfaces with Occlusions from Layered Stereo," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1073-1078, Aug. 2004.
37. T. Meltzer, C. Yanover, and Y. Weiss, "Globally Optimal Solutions for Energy Minimization in Stereo Vision Using Reweighted Belief Propagation," Proc. 11th IEEE Int'l Conf. Computer Vision, pp. 428-435, 2005.
38. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
39. C. Rother, V. Kolmogorov, and A. Blake, "'GraphCut' Interactive Foreground Extraction Using Iterated Graph Cuts," ACM Trans. Graphics, vol. 23, no. 3. pp 309-314, 2004.
40. C. Rother, V. Kolmogorov, V. Lempitsky, and M. Szummer, "Optimizing Binary MRFs via Extended Roof Duality," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, June 2007.
41. C. Rother, S. Kumar, V. Kolmogorov, and A. Blake, "Digital Tapestry," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 589-596, 2005.
42. D. Scharstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithm," Int'l J. Computer Vision vol. 47, nos. 1-3, pp. 7-42, 2002.
43. D. Snow, P. Viola, and R. Zabih, "Exact Voxel Occupancy with Graph Cuts," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition pp. 345-352, 2000.
44. R. Szeliski, "Locally Adapted Hierarchical Basis Preconditioning," ACM Trans. Graphics, vol. 25. no. 3. pp. 1135-1143, 2006.
45. M. Tappen and W. Freeman, "Comparison of Graph Cuts with Belief Propagation for Stereo, Using Identical MRF Parameters," Proc. Ninth IEEE Int'l Conf. Computer Vision, pp. 900-907, 2003.
46. O. Veksler, "Graph Cut Based Optimizztion for MRFS with Truncated Convex Priors," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2007.
47. M. Wainwright, T. Jaakkola, and A. Willsky, "Tree Consistency and Bounds on the Performance of the Max-Product Algorithm and its Generalizations," Statistics and Computing, vol. 14, pp. 143-166, Apr. 2004.
48. M. Wainwright, T. Jaakkola, and A. Willsky, "Map Estimation via Agreement on Trees: Message-Passing and Linear Programming," IEEE Trans. Information Theory, vol. 51, no. 11, pp. 3697-3717, 2005.
49. Y. Weiss and W. Freeman, "On the Optimality of Solutions of the Max-Product Belief Propagation Algorithm in Arbitrary Graphs," IEEE Trans. Information Theory, vol. 47, no. 2, pp. 723-735, 2001.
50. J. Yedidia, W. Freeman, and Y. Weiss, "Generalized Belief Propagation," Advances in Neural Information Processing Systems, pp. 689-695, 2000.