Semi-supervised segmentation based on non-local continuous min-cut

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5567 LNCS, Issue , 2009, Pages 112-123

  Houhou, Nawal ^a   Bresson, Xavier ^b   Szlam, Arthur ^b   Chan, Tony F ^b   Thiran, Jean Philippe ^a  

^a EPFL   (Switzerland)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION PROCESS; ENERGY MINIMIZATION; IMAGE INFORMATION; IMAGE INTENSITIES; ITERATIVE SCHEMES; MIN-CUT; MIN-CUT ALGORITHM; NONLOCAL; REAL-WORLD IMAGE; SEED POINT; SEGMENTATION PROCESS; SEMI-SUPERVISED; THREE-TERM;

COMPUTER VISION; DIFFUSION; TEXTURES;

LABELS;

EID: 69049106813     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02256-2_10     Document Type: Conference Paper

References (28)

1. Strang, G.: Maximal Flow Through A Domain. Mathematical Programming 26(2), 123-143 (1983)
2. Protiere, A., Sapiro, G.: Interactive image segmentation via adaptive weighted distances. IEEE Transactions on Image Processing 16(4), 1046-1057 (2007)
3. Cremers, D., Fluck, O., Rousson, M., Aharon, S.: A Probabilistic Level Set Formulation for Interactive Organ Segmentation. In: SPIE (2007)
4. Appleton, B., Talbot, H.: Globally minimal surfaces by continuous maximal flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 106-118 (2006)
5. Unger, M., Pock, T., Cremers, D., Bischof, H.: Tvseg - interactive total variation based image segmentation. In: British Machine Vision Conference (BMVC), Leeds, UK (September 2008)
6. Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4(2), 490-530 (2005)
7. Gilboa, G., Osher, S.: Nonlocal linear image regularization and supervised segmentation. Multiscale Modeling and Simulation 6(2), 595-630 (2007)
8. Elias, P., Feinstein, A., Shannon, C.E.: Note on Maximum Flow Through a Network. IRE Transactions on Information Theory 2, 117-119 (1956)
9. Efros, A., Leung, T.: Texture Synthesis by Non-Parametric Sampling. In: IEEE International Conference on Computer Vision, vol. 2, pp. 10-33 (1999)
10. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888-905 (2000)
11. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43(1), 7-27 (2001)
12. Efros, A., Freeman, W.: Image quilting for texture synthesis and transfer. In: Proceedings of the Conference on Computer graphics and interactive techniques, SIGGRAPH, pp. 341-346. ACM, New York (2001)
13. Liang, L., Liu, C., Xu, Y., Guo, B., Shum, H.: Real-time texture synthesis by patch-based sampling. ACM Trans. Graph. 20(3), 127-150 (2001)
14. Bresson, X., Chan, T.: Non-local Unsupervised Variational Image Segmentation Models, UCLA CAM Report 08-67 (2008)
15. Wu, Z., Leahy, R.: An Optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101-1113 (1993)
16. Ishikawa, H., Geiger, D.: Segmentation by grouping junctions. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 125-131 (1998)
17. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proceedings of Eighth IEEE International Conference on Computer Vision, vol. 1, pp. 105-112 (2001)
18. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision 70(2), 109-131 (2006)
19. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 2001 (2001)
20. Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: Image and video synthesis using graph cuts. In: Proceedings of the Conference on Computer graphics and interactive techniques, SIGGRAPH, vol. 22(3), pp. 277-286 (July 2003)
21. Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 19-26. Morgan Kaufmann Publishers Inc., San Francisco (2001)
22. Yu, S., Shi, J.: Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 173-183 (2004)
23. Grady, L., Funka-lea, G.: Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In: Proceedings of the European Conference on Computer Vision, pp. 230-245. Springer, Heidelberg (2004)
24. Zhou, D., Scholkopf, B.: A Regularization Framework for Learning from Graph Data. In:Workshop on Statistical Relational Learning and Its Connections to Other Fields (2004)
25. Unger, M., Pock, T., Bischof, H.: Continuous globally optimal image segmentation with local constraints. In: Computer Vision Winter Workshop 2008 (2008)
26. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. International Journal of Computer Vision 22(1), 61-79 (1997)
27. Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision 20(1-2), 89-97 (2004)
28. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, July 2001, vol. 2, pp. 416-423 (2001)