A global perspective on MAP inference for low-level vision

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 2319-2326

  Woodford, Oliver J ^a   Rother, Carsten ^b   Kolmogorov, Vladimir ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b MICROSOFT RESEARCH   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DE FACTO; EXTENSIBLE FRAMEWORK; FLOW ALGORITHM; GLOBAL OPTIMALITY; GLOBAL PERSPECTIVE; HEAVY-TAILED; IMAGE DE-NOISING; LOW-LEVEL VISION; MARGINAL PROBABILITY; MARKOV RANDOM FIELD; MAXIMUM A POSTERIORI; NP-HARD; PROBABILISTIC MODELS; TEXTURE SYNTHESIS;

COMPUTER VISION; DELTA FUNCTIONS;

STRUCTURAL FRAMES;

EID: 77953223384     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459434     Document Type: Conference Paper

References (27)

1. R. Ahuja, T. Magnanti, and J. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
2. R. K. Ahuja, J. B. Orlin, and C. Stein. Improved algorithms for bipartite network flow. SIAM J. Computing, 23(5), 1994.
3. D. Bertsekas. Nonlinear Programming. A. Scientific, 99.
4. D. Cremers and L. Grady. Learning statistical priors for efficient combinatorial optimization via graph cuts. In ECCV, 2006.
5. C. Fox and G. Nicholls. Exact MAP States and Expectations from Perfect Sampling: Greig, Porteous and Seheult revisited. In AIP conf.
6. D. Freedman and T. Zhang. Active contours for tracking distributions. PAMI, 13(4), 2004.
7. A. Gagalowicz and S. D. Ma. Sequential synthesis of natural textures. Comp. vision, graphics, and image proc., 1985.
8. R. Gupta, A. Diwan, and S. Sarawagi. Efficient inference with cardinality-based clique potentials. In ICML, '07.
9. P. Kohli, L. Ladicky, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. In CVPR, 2008.
10. V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. PAMI, 28(10), 2006.
11. V. Kolmogorov, Y. Boykov, and C. Rother. Applications of parametric maxflow in computer vision. In ICCV, 2007.
12. V. Kolmogorov and C. Rother. Minimizing non-submodular functions with graph cuts - a review. PAMI, 29(7):1274-1279, 2007.
13. N. Komodakis, N. Paragios, and G. Tziritas. MRF optimization via dual decomposition: Message-passing revisited. In ICCV, 2007.
14. J. Kopf, C.-W. Fu, D. Cohen-Or, O. Deussen, D. Lischinski, and T.-T. Wong. Solid Texture Synthesis from 2D Exemplars. SIGGRAPH, 26(3):2:1-2:9, 2007.
15. V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick. Graphcut textures: Image and video synthesis using graph cuts. SIGGRAPH, July 2003.
16. C. Rother, V. Kolmogorov, and A. Blake. Grabcut - interactive foreground extraction using iterated graph cuts. SIGGRAPH, August 2004.
17. C. Rother, V. Kolmogorov, T. Minka, and A. Blake. Cosegmentation of image pairs by histogram matching - incorporating a global constraint into MRFs. In CVPR, 06.
18. M. I. Schlesinger and V. V. Giginyak. Solution to structural recognition (MAX,+)-problems by their equivalent transformations. Part 1. Contr. Syst. and Comp., (1).
19. M. I. Schlesinger and V. V. Giginyak. Solution to structural recognition (MAX,+)-problems by their equivalent transformations. Part 2. Contr. Syst. and Comp., (2).
20. E. Sudderth and M. Jordan. Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. In NIPS, 08.
21. Z. Tu and S.-C. Zhu. Image Segmentation by Data-Driven Markov Chain Monte Carlo. PAMI, 24(5), 2002.
22. S. Vicente, V. Kolmogorov, and C. Rother. Joint optimization of segmentation and appearance models. In ICCV, 2009.
23. M. Wainwright, T. Jaakkola, and A. Willsky. MAP estimation via agreement on trees: Message-passing and linear-programming approaches. IEEE Trans. Information Theory, 51(11), 2005.
24. T. Werner. High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF). In CVPR, 2008.
25. O. J.Woodford, C. Rother, and V. Kolmogorov. A global perspective on map inference for low-level vision. In Microsoft Research Technical Report, 2009.
26. A. Zalesny and L. V. Gool. A compact model for viewpoint dependent texture synthesis. In SMILE Workshop, Lecture notes in computer science, volume 2018, 2001.
27. S.-C. Zhu, Y. Wu, and D. Mumford. FRAME: Filters, Random fields And Maximum Entropy - towards a unified theory for texture modeling. IJCV, 27:1-20, 1998.