Normalized tree partitioning for image segmentation

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Wang, Jingdong ^a   Jia, Yangqing ^b   Hua, Xian Sheng ^a   Zhang, Changshui ^b   Quan, Long ^c  

^a MICROSOFT RESEARCH ASIA   (China)

^b TSINGHUA UNIVERSITY   (China)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATION; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMBINATORIAL MATHEMATICS; COMPUTATIONAL GEOMETRY; COMPUTATIONAL METHODS; COMPUTER VISION; DIGITAL IMAGE STORAGE; FEATURE EXTRACTION; FLOW OF SOLIDS; GRAPH THEORY; IMAGE PROCESSING; IMAGE SEGMENTATION; PATTERN RECOGNITION;

AGGREGATION METHODS; BIPARTITION; BIPARTITIONING; BOTTOM-UP; CLUSTERING PERFORMANCE; COMBINATORIAL ALGORITHMS; COMPUTATIONAL COSTS; DATA GRAPHS; GLOBAL CRITERION; GRAPH-BASED; GRAPH-BASED CLUSTERING; MINIMUM ENTROPY; NORMALIZED CUT; PROBABILISTIC METHODS; RECURSIVE BIPARTITIONING; SPECTRAL CLUSTERING; SUPERIOR PERFORMANCE; TREE PARTITIONING;

TREES (MATHEMATICS);

EID: 51949087231     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587454     Document Type: Conference Paper

References (17)

1. S. Agarwal, J. Lim, L. Zelnik-Manor, P. Perona, D. J. Kriegman, and S. Belongie. Beyond Pairwise Clustering. In CVPR (2), pages 838-845, 2005.
2. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y.Wu. An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions. J. ACM, 45(6):891-923, 1998.
3. C. K. Chow and C. N. Liu. Approximating Discrete Probability Distributions with Dependence Trees. IEEE Trans. Information Theory, 14(3):462-467, May 1968.
4. D. Comaniciu and P. Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603-619, 2002.
5. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient Graph-Based Image Segmentation. Int. J. Comput. Vision, 59(2):167-181, 2004.
6. J. Freixenet, X. Muñoz, D. Raba, J. Martí, and X. Cufí. Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In ECCV (3), pages 408-422, 2002.
7. B. Frey and D. Dueck. Clustering by Passing Messages Between Data Points. Science, 315(5814):972-976, 2007.
8. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988.
9. D. R. Martin, C. Fowlkes, D. Tal, and J. Malik. A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In ICCV, pages 416-425, 2001.
10. M. Meila. Comparing Clusterings: an Axiomatic View. In ICML, pages 577-584, 2005.
11. J. Shi and J. Malik. Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888-905, 2000.
12. R. Unnikrishnan, C. Pantofaru, and M. Hebert. Toward Objective Evaluation of Image Segmentation Algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 29(6):929-944, 2007.
13. R. Urquhart. Graph Theoretical Clustering Based on Limited Neighborhood Sets. Pattern Recognition, 15(3):173-187, 1982.
14. S. X. Yu and J. Shi. Multiclass Spectral Clustering. In ICCV, pages 313-319, 2003.
15. C. Zahn. Graph Theoretic Methods for Detecting and Describing Gestalt Clusters. IEEE Trans. Computers, 20.
16. L. Zelnik-Manor and P. Perona. Self-Tuning Spectral Clustering. In NIPS, 2004.
17. D. Zhou, J. Huang, and B. Schölkopf. Learning with Hypergraphs: Clustering, Classification, and Embedding. In NIPS, pages 1601-1608, 2006.