Affinity learning via self-diffusion for image segmentation and clustering

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 2312-2319

  Wang, Bo ^a   Tu, Zhuowen ^b,c  

^a UNIVERSITY OF TORONTO   (Canada)

^b MICROSOFT RESEARCH ASIA   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CRITICAL TIME; DATA POINTS; DIFFUSION PROCESS; GRAPH-BASED; IMAGE CLUSTERING; LEARNING METHODS; NORMALIZED CUTS; PARAMETER-TUNING; QUALITY OF CLUSTERING; SEGMENTATION AND CLUSTERING; SEGMENTATION RESULTS; SELF-DIFFUSION;

ALGORITHMS; COMPUTER VISION; DIFFUSION;

IMAGE SEGMENTATION;

EID: 84866708964     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247942     Document Type: Conference Paper

References (38)

1. X. Bai, X. Yang, L. Latecki, W. Liu, and Z. Tu. Learning context sensitive shape similarity by graph transduction. IEEE Trans. PAMI, 2010.
2. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. of Machine Learning Research, 7:2399-2434, 2006.
3. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Trans. PAMI, 24:705-522, 2002.
4. A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr. Interactive image segmentation using an adaptive gmmrf model. In in ECCV, 2004.
5. R. Coifman and S. Lafon. Diffusion maps. Applied and Comp. Harmonic Ana., 2006.
6. D. Comaniciu, P. Meer, and S. Member. Mean shift: A robust approach toward feature space analysis. IEEE Tran. on PAMI, 24:603-619, 2002.
7. T. Cour, F. Benezit, and J. Shi. Spectral segmentation with multiscale graph decomposition. In Proc. of CVPR, pages 1124-1131, 2005.
8. Y. Deng and B. S. Manjunath. Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. Pattern Anal. Mach. Intell., 23(8):800-810, 2001.
9. M. Donoser, M. Urschler, M. Hirzer, and H. Bischof. Saliency driven total variation segmentation. 2009.
10. O. Duchenne, J.-Y. Audibert, R. Keriven, J. Ponce, and F. Segonne. Efficient planar graph cuts with applications in computer vision. In Proc. of CVPR, 2008.
11. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 59:2004, 2004.
12. F. Fouss, A. Pirotte, J. michel Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation. IEEE Tran. on Know. and Data Eng., 19:2007, 2006.
13. J. Freixenet, X. Mu?oz, D. Raba, J. Martł, and X. Cufł. Yet another survey on image segmentation: Region and boundary information integration. In In ECCV, pages 408-422, 2002.
14. A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Tran. on PAMI, 23(6):643-660, 2001.
15. L. Grady. Random Walks for Image Segmentation. IEEE Tran. on PAMI, 28(11):1768-1783, 2006.
16. J. Jiang, B. Wang, and Z. Tu. Unsupervised metric learning by self-smoothing operator. In ICCV, 2011.
17. T. H. Kim and K. M. Lee. Learning full pairwise affinities for spectral segmentation. CVPR, pages 2101-2108, 2010.
18. T. H. Kim, K. M. Lee, and S. U. Lee. Nonparametric higher-order learning for interactive segmentation. CVPR, pages 3201-3208, 2010.
19. P. Kohli and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. In In CVPR, 2008.
20. D. 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.
21. M. Meila. Comparing clusterings: an axiomatic view. In In Proceedings of the 22nd international conference on Machine learning, pages 577-584. ACM Press, 2005.
22. S. A. Nene, S. K. Nayar, and H. Murase. Columbia Object Image Library (COIL-20). Technical report, Feb 1996.
23. C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM SIGGRAPH, 2004.
24. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
25. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
26. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. on PAMI, 22:888-905, 1997.
27. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008.
28. A. Strehl, J. Ghosh, and C. Cardie. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3:583-617, 2002.
29. M. Szummer and T. Jaakkola. Partially labeled classification with markov random walks. In NIPS, pages 945-952, 2001.
30. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2000.
31. N. Tishby and N. Slonim. Data clustering by markovian relaxation and the information bottleneck method. In Proc. of NIPS, 2000.
32. Z. Tu and S. Zhu. Image segmentation by data-driven markov chain monte carlo. IEEE Trans. on PAMI, 2002.
33. R. Unnikrishnan, C. Pantofaru, and M. Hebert. Toward Objective Evaluation of Image Segmentation Algorithms. IEEE Tran. on PAMI, 29(6):929-944, 2007.
34. J.Wang, Y. Jia, X.-S. Hua, C. Zhang, and L. Quan. Normalized tree partitioning for image segmentation. CVPR, pages 1-8, 2008.
35. L. Zelnik-Manor and P. Perona. Self-tuning spectral clustering. In NIPS, 2004.
36. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf. Ranking on data manifolds. In Proc. NIPS, 2004.
37. X. Zhu. Semi-supervised learning with graphs. In Doctoral Dissertation, CMULTI-05-192, 2005.
38. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML, 2003.