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| Image Segmentation Using Generalized Integrated Squared Error-Based Eigenvector Selection |
| ZHANG Da-Ming1,2, FU Mao-Sheng1, LUO Bin1 |
1.School of Computer Science and Technology, Anhui University, Hefei 230039
2.Department of Mathematics Physics, Anhui Institute of Architecture Industry, Hefei, 230022 |
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Abstract Not all of the top eigenvectors contain clustering information for the task of real-world data clustering. Since the noise exists, the distribution of elements of an eigenvector is complex and it is necessary to select eigenvectors for spectral clustering. In this paper, the integrated squared error (ISE) divergence is generalized and the proposed generalized integrated squared error (GISE) is used to estimate the multimodality of data distribution and measure the clustering information of eigenvector. Then, a spectral clustering algorithm based on eigenvector selection is proposed. The experimental results on varied natural images segmentation show that the proposed algorithm is simpler and more effective than pervious algorithms.
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Received: 15 July 2009
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[1] Shi Jianbo, Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905
[2] Ng A, Jordan M, Weiss Y. On Spectral Clustering: Analysis and an Algorithm // Proc of the Conference on Neural Information Processing Systems. Vancouver, Canada, 2002: 849-856
[3] Wang Song, Siskund J M. Image Segmentation with Ratio Cut. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(6): 675-690
[4] Grady L, Schwartz E L. Isoperimetric Graph Partitioning for Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(3): 469-475
[5] Zelnik-Manor L, Perona P. Self-Tuning Spectral Clustering // Proc of the Conference on Neural Information Processing Systems. Vancouver, Canada, 2005:1601-1608
[6] Verman D, Meila M. A Comparison of Spectral Clustering Algorithms. Technical Report, 03-05-01, Seattle, USA, University of Washington. Department of Computer Science, 2003
[7] Xiang Tao, Gong Shaogang. Spectral Clustering with Eigenvector Selection. Pattern Recognition, 2008, 41(3): 1012-1029
[8] Schwartz G. Estimating the Dimension of A Model. The Annals of Statistics, 1978, 6(2): 461-464
[9] McLachlan G J, Krishnan T. The EM Algorithm and Extensions. New York, USA: Wiley, 1997
[10] McLachlan G J, Peel D. Finite Mixture Models. New York, USA: Wiley, 2000
[11] Principe J, Xu D, Fisher J. Information Theoretic Learning // Haykin S, ed. Unsupervised Adaptive Filtering, New York, USA: John Wiley Sons, 2000: 265-317
[12] Malik J, Belongie S, Leung T, et al. Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision, 2001, 43(1): 7-27
[13] MATLAB Normalized Cuts Segmentation Code [DB/OL]. [2009-04-20]. http://www.cis.upenn.edu/~jshi/software/
[14] Martin D, Fowlkes C, Tal D, et al. A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics // Proc of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada, 2001, Ⅱ: 416-423 |
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2011, Vol. 24 
