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An initialization-free clustering technique based on symmetry

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EN
Abstrakty
EN
In this paper we propose a clustering technique that extracts sub-clusters based on a simple measure of isotropic symmetry. These sub-clusters are then used as building blocks to form final clusters of any arbitrary shape including concave ones through merging iteratively. The proposed method is tested on multi-spectral satellite imagery and a good result is obtained. Major advantages of this method are its simplicity and being free from initial guess about the cluster centres, shapes and the number of clusters. However, this algorithm is more suitable for mul-tivariate images even with very high spectral resolution.
Twórcy
autor
autor
  • Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700 108, pinak@isical.ac.in
Bibliografia
  • [1] M. B. Al-Daoud and S. A. Roberts. New methods for the initialization of clusters. Pattern Recognition Letters, 17:451-455, 1996.
  • [2] R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley, New York, 1973.
  • [3] I. Gath, A. S. Iskoz and B. Van. Cutsem Data induced metric and fuzzy clustering of non-convex patterns of arbitrary shape Pattern Recognition, 18(6):541-553, 1997.
  • [4] J. Hartigan. Clustering Algorithms. John Wiley, New York, 1988.
  • [5] A. K. Jain and R. C. Dubes. Algorithm for clustering. Prentice Hall, Englewood Cliffs, N. J, 1988.
  • [6] A. K. Jain, M. N. Murty and P. J. Flynn. Data clustering: a review. ACM Computing Surveys, 31 (3):264-323, 1999.
  • [7] K. Kanatani. Comments on symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(3):246-247, 1997.
  • [8] J. Puzicha, T. Hofmann, and J. M. Buhmann. A theory of proximity based clustering: structure detection by optimization. Pattern Recognition, 33:617-634, 2000.
  • [9] M. C. Su and C. H. Chou. A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):674-680, 2001.
  • [10] T. N. Tran, R. Wehrens and L. M. C. Buydens. Clustering multispectral images: a tutorial. Chemetrics and Intelligent Laboratory Systems, 77:3-17, 2004.
  • [11] A. R. Webb. Statistical Pattern Recognition, 2nd Edition. Wiley, Malven, U.K., 2002.
  • [12] D. Zabrodsky, S. Peleg, and D. Avnir. Symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12):1154-1166, 1995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0032-0002
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