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Incremental EM algorithm with multiresolution kd-trees and cluster validation and its application to image segmentation
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 Title & Authors
Incremental EM algorithm with multiresolution kd-trees and cluster validation and its application to image segmentation
Lee, Kyoung-Mi;
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In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.
Clustering;EM Algorithm;Kd-tree;Cluster Validate;Image Segmentation;
 Cited by
A.P. Dempster, N.M. Laird and D.B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm (with discussion)," Journal of the royal statistical society B, vol. 39, no. 1, pp. 1-38, 1977.

S.J. Nowlan, "Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures," PhD thesis, School of Computer Science, Carnegie Mellon University, 1991.

J. Xu, G. Ye, Y. Wang, G. Herman, B. Zhang and J. Yang, "Incremental EM for probabilistic latent semantic analysis on human action recognition," Proceedings of the IEEE conference on advanced video and signal based surveillance, 2009.

S.-K. Ng, G.J. McLachlan and A.H. Lee, "An incremental EM-based learning approach for online prediction of hospital resource utilization," Artificial Intelligence in Medicine, vol. 36, pp. 257-267, 2006. crossref(new window)

K.-M. Lee, "Elliptical clustering with incremental growth and its application to skin color region segmentation," Journal of the Korean Information Science Society, vol. 31, no. 9, pp. 1161-1170, 2004.

S.-S. Kim and J.-H. Kang, "Improved expectation and maximization via a new method for initial values," Journal of the Korean Institute of Intelligent Systems, vol. 13, no. 4, pp. 416-426, 2003. crossref(new window)

L. Xu, M.I. Jordan, and G.E. Hinton, "An alternative model for mixtures of experts," Advances in neural information processing systems, vol. 7, pp. 633-640, 1995.

A.W. Moore, "Very fast EM-based mixture model clustering using multiresolution kd-tree," Advances in neural information processing systems, vol. 11, pp. 543-549, 1999.

S.-K. Ng and G.J. McLachlan, "Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images," Pattern recognition, vol. 37, no. 8, pp. 1573-1589, 2004. crossref(new window)

J. Platt, "A resource-allocating network for function interpolation," Neural computation, vol. 3, no. 2, pp. 213-225, 1991. crossref(new window)

S.-B. Roh, T.-C. Ahn, Y.-S. Baek and Y.-S. Kim, "Space partition using context fuzzy c-Means algorithm for image segmentation," Journal of the Korean Institute of Intelligent Systems, vol. 20, no. 3, pp. 368-374, 2010. crossref(new window)

P. Brodatz, Textures: A photographic album for artists and designers, Dover publications, NY, 1966.

J.G. Daugman, "An information- theoretic view of analogue representation in striate cortex," Computational neuroscience, In E.L. Schwartz (Eds.), Cambridge, MA: MIT Press, pp. 403-424, 1990.

T. Randen and J.H. Husoy, "Filtering for texture classification: a comparative study," IEEE transactions on pattern analysis and machine intelligence, vol. 21, no. 4, pp. 291-310, 1999. crossref(new window)