Multi-label MRF optimization via a least squares s-t cut

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

Volumn 5875 LNCS, Issue PART 1, 2009, Pages 1055-1066

  Hamarneh, Ghassan ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION APPLICATIONS; EDGE WEIGHTS; GRAPH CUT; LEAST SQUARE; LEAST SQUARES SOLUTIONS; MARKOV RANDOM FIELDS; MULTI-LABEL; QUALITATIVE IMAGES; SEGMENTATION RESULTS;

COMPUTER APPLICATIONS; COMPUTER SCIENCE; COMPUTER VISION; GRAPH THEORY; HIDDEN MARKOV MODELS; IMAGE SEGMENTATION; OPTIMIZATION; STRUCTURAL FRAMES;

LABELS;

EID: 72449184779     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10331-5_98     Document Type: Conference Paper

References (16)

1. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23, 1222-1239 (2001)
2. Grady, L.: Random walks for image segmentation. IEEE PAMI 28, 1768-1783 (2006)
3. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision 70, 109-131 (2006)
4. Ishikawa, H.: Exact optimization for markov random fields with convex priors. IEEE PAMI 25, 1333-1336 (2003)
5. Schlesinger, D., Flach, B.: Transforming an arbitrary min-sum problem into a binary one. Tech. Report TUD-FI06-01, Dresden University (2006)
6. Veksler, O.: Graph cut based optimization for MRFs with truncated convex priors. In: IEEE CVPR, pp. 1-8 (2007)
7. Lempitsky, V., Rother, C., Blake, A.: Logcut - efficient graph cut optimization for markov random fields. In: IEEE ICCV, pp. 1-8 (2007)
8. Komodakis, N., Tziritas, G., Paragios, N.: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primaldual strategies. Comput. Vis. Image Underst. 112, 14-29 (2008)
9. Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE PAMI 28, 1568-1583 (2006)
10. Kohli, P., Shekhovtsov, A., Rother, C., Kolmogorov, V., Torr, P.: On partial optimality in multi-label MRFs. In: International Conference on Machine learning (ICML), pp. 480-487. ACM, New York (2008)
11. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE PAMI 30, 1068-1080 (2008)
12. Ramalingam, S., Kohli, P., Alahari, K., Torr, P.: Exact inference in multi-label CRFs with higher order cliques. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1-8 (2008)
13. Björk, A.: Numerical Methods for Least Squares Problem. SIAM, Philadelphia (1996)
14. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 279-302 (1945)
15. Cocosco, C.A., Kollokian, V., Kwan, R.K.-s., Pike, G.B., Evans, A.C.: Brainweb: Online interface to a 3D mri simulated brain database. NeuroImage 5, 425 (1997)
16. Lawson, C., Hanson, R.: Solving Least Squares Problems. Society for Industrial Mathematics (1974)