Learning CRFs using graph cuts

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

Volumn 5303 LNCS, Issue PART 2, 2008, Pages 582-595

  Szummer, Martin ^a   Kohli, Pushmeet ^a   Hoiem, Derek ^b  

^a MICROSOFT RESEARCH   (United Kingdom)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; GRAPH ALGORITHMS; GRAPHIC METHODS; INFERENCE ENGINES; PARAMETER ESTIMATION; STEREO IMAGE PROCESSING;

COMPUTER VISION PROBLEMS; FAST INFERENCE; MULTI-CLASS PROBLEMS; OBJECTIVE FUNCTIONS; OPTIMAL PARAMETER; PARAMETER LEARNING; SPEED-UP TECHNIQUES; STEREO RECONSTRUCTION;

IMAGE SEGMENTATION;

EID: 56749103990     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88688-4_43     Document Type: Conference Paper

References (17)

1. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147-159 (2004)
2. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222-1239 (2001)
3. Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV, pp. I: 105-112 (2001)
4. Rother, C., Kolmogorov, V., Blake, A.: "GrabCut": interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics 23(3), 309-314 (2004)
5. Barbu, A., Zhu, S.C.: Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1239-1253 (2005)
6. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M.F., Rother, C.: A comparative study of energy minimization methods for Markov random fields. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 16-29. Springer, Heidelberg (2006)
7. Kumar, S., August, J., Hebert, M.: Exploiting inference for approximate parameter learning in discriminative fields: An empirical study. In: EMMCVPR, pp. 153-168 (2005)
8. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Jrnl. Machine Learning Research 6, 1453-1484 (2005)
9. Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.Y.: Discriminative learning of Markov random fields for segmentation of 3D scan data. In: CVPR, pp. 169-176 (2005)
10. Taskar, B., Chatalbashev, V., Koller, D., Guestrin, C.: Learning structured prediction models: a large margin approach. In: ICML, pp. 896-903 (2005)
11. Boros, E., Hammer, P.L.: Pseudo-boolean optimization. Discrete Applied Mathematics 123(1-3), 155-225 (2002)
12. Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: ICCV (2005)
13. Finley, T., Joachims, T.: Training structural SVMs when exact inference is intractable. In: Proc. Intl. Conf. on Machine Learning (ICML), pp. 304-311 (2008)
14. Komodakis, N., Tziritas, G., Paragios, N.: Fast, approximately optimal solutions for single and dynamic MRFs. In: CVPR (2007)
15. Collins, M.: Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: ACL-02 conf. Empirical methods in natural lang. processing (EMNLP), pp. 1-8 (2002)
16. Taskar, B., Chatalbashev, V., Koller, D.: Learning associative Markov networks. In: ICML (2004)
17. Kohli, P., Torr, P.H.S.: Measuring uncertainty in graph cut solutions - efficiently computing min-marginal energies using dynamic graph cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 30-43. Springer, Heidelberg (2006)