Image segmentation using a weighted kernel PCA approach to spectral clustering

Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007

Volumn , Issue , 2007, Pages 208-213

  Alzate, Carlos ^a   Suykens, Johan A K ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; COLOR IMAGE PROCESSING; COMPUTER SIMULATION; GRAPH THEORY; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS;

DATA-DRIVEN MATRIX; OUT-OF-SAMPLE EXTENSION;

IMAGE SEGMENTATION;

EID: 34548706096     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIISP.2007.369319     Document Type: Conference Paper

References (19)

1. C. Alzate and, J. A. K. Suykens. Kernel principal component analysis using an epsilon insensitive robust loss function. Internal report 06-03. Submitted for publication, ESAT-SISTA, K. U. Leuven, 2006.
2. C. Alzate and J. A. K. Suykens. A weighted kernel pca formulation with out-of-sample extensions for spectral clustering methods. In Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN'06), pages 138-144, 2006.
3. F. R. Bach and M. I. Jordan. Learning spectral clustering. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, Cambridge, MA, 2004. MIT Press.
4. Y. Bengio, J. F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet. Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, Cambridge, MA, 2004. MIT Press.
5. P. K. Chan, M. D. F. Schlag, and J. Y. Zlen. Spectral k-way ratiocut partitioning and clustering. IEEE Trans. Computer-Aided Design, 13(9):1088-1096, 1994.
6. C. Ding and X. He. K-means clustering via principal component analysis. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 29, New York, NY, USA, 2004. ACM Press.
7. C. Ding and X. He. Linearized cluster assignment via spectral ordering. In ICML '04: Proceedings of the twenty-first International Conference on Machine Learning, page 30, New York, NY, USA, 2004. ACM Press.
8. K. Fan. On a theorem, of weyl concerning eigenvalues of linear transformations. Proc. Natl. Acad. Sci. USA, 35(11):652-655, 1949.
9. M. Fiedler. A property of eigenvectors of nonnegative symmetric matrices and its applications to graph theory. Czech. Math. J., 25(100):619-633, 1975.
10. I. Fisher and J. Poland. Amplifying the block matrix structure for spectral clustering. In Proceedings of the 14th Annual Machine Conference of Belgium and the Netherlands, pages 21-28, 2005.
11. M. Gu, H. Zha, C. Ding, X. He, and H. Simon. Spectral relaxation models and structure analysis for k-way graph clustering and biclustering. Technical report, Penn. State Univ, Computer Science and Engineering., 2001.
12. 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 Proc. 8th Int'l Conf. Computer Vision, volume 2, pages 416-423, July 2001.
13. M. Meila and J. Shi. Learning segmentation, by random walks. In T.K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13. MIT Press, 2001.
14. M. Meila and J. Shi. A random walks view of spectral segmentation. In T. Jaakkola and T. Richardson, editors, Artificial Intelligence and Statistics AISTATS, 2001.
15. B. Schölkopf, A. Smola, and K. R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
16. J. Shi and J. Malik. Normalized, cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell., 22(8):888-905, 2000.
17. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle. Least Squares Support Vector Machines. World Scientific, Singapore, 2002.
18. J. A. K. Suykens, T. Van, Gestel, J. Vandewalle, and B. De Moor. A support vector machine formulation to PCA analysis and its kernel version. IEEE Trans. Neural Networks, 14(2):447-450, March 2003.
19. S. Wang and J. M. Siskind. Image segmentation, with ratio cut. IEEE Trans. Pattern Anal. Machine Intell., 25(6):675-690, 2003.