A new framework for approximate labeling via graph cuts

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1018-1025

  Komodakis, Nikos ^a   Tziritas, Georgios ^a  

^a UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE LABELING; COMBINATORIAL ALGORITHMS; GRAPH CUTS; NP HARD CLASSIFICATION;

ALGORITHMIC LANGUAGES; APPROXIMATION THEORY; GRAPH THEORY; LINEAR PROGRAMMING; OPTIMIZATION; PROBLEM SOLVING;

CLASSIFICATION (OF INFORMATION);

EID: 33745911953     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.14     Document Type: Conference Paper

References (9)

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3. J. Kleinberg and E. Tardos. Approximation algorithms for classification problems with pairwise relaionships: metric labeling and markov random fields. Journal of the ACM, 2002.
4. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? In ECCV, 2002.
5. N. Komodakis and G. Tziritas. Approximate labeling via the primal-dual schema. Technical Report CSD-TR-05-01, Computer Science Department, February 2005.
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7. V. Vazirani. Approximation Algorithms. Springer, 2001.
8. O. Veksler. Efficient graph-based energy minimization methods in computer vision. PhD thesis, Cornell University, 1999.
9. R. Zabih and V. Kolmogorov. Spatially coherent clustering using graph cuts. In CVPR, 2004.