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Eigen Value based K-means Clustering for Image Compression

K. Somasundaram, M. Mary Shanthi Rani Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450583
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  1. K Somasundaram and Mary Shanthi M Rani. Article: Eigen Value based K-means Clustering for Image Compression. International Journal of Applied Information Systems 3(7):21-24, August 2012. BibTeX

    @article{key:article,
    	author = "K. Somasundaram and M. Mary Shanthi Rani",
    	title = "Article: Eigen Value based K-means Clustering for Image Compression",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 7,
    	pages = "21-24",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

In this paper, a new method has been proposed to enhance the performance of K-means clustering using the significance of Eigen values in spectral decomposition. Experimental results with standard images show that the proposed method shows faster convergence and reduced bit rate than standard K-means without compromise in the quality of the reconstructed images measured in terms of Peak Signal to Noise Ratio(PSNR).

Reference

  1. Ankerst, M. , M. Breunig, H. P. Kriegel and J. Sander, "OPTICS: Ordering points to identify the clustering structure", Proceedings of ACM SIGMOD International Conference on Management of Data Mining, ACM Press, Philadelphia, Pennsylvania, United States, pp. 49-60, June 1999.
  2. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, "When is 'nearest neighbor' meaningful?", Proceedings of the International Conference on Database Theory, Jerusalem, pp. 217–235, January 1999.
  3. Linde Y. , Buzo A. , and Gray R. M. , "An Algorithm for Vector Quantizer Design", IEEE Transactions on Communication, Vol. 28, pp. 84–95, 1980.
  4. N. Venkateswaran, and Y. V. Ramana Rao, "K-Means Clustering Based Image Compression in Wavelet Domain", Information Technology Journal , Vol. 6, pp. 148–153, 2007.
  5. Gersho A. , and Gray R. M. "Vector Quantization and Signal compression", Kluwer Academic Publishers, New York, pp. 761, 1992.
  6. H. P. Ng, S. H. Ong, K. W. C. Foong, P. S. Goh, and W. L. Nowinski, "Medical image segmentation using k-means clustering and improved watershed algorithm", IEEE Southwest Symposium on Image Analysis and Interpretation, Denver, pp. 61-65, 2006.
  7. Duda, R. O. and P. E. Hart, Pattern Classification and Scene Analysis. John Wiley Sons, New York, pp. 482, 1973.
  8. Jiang, D. J. Pei and A. Zhang, "An interactive approach to mining gene expression data", IEEE Transactions on Knowledge and Data Engineering, Vol. 17, pp. 1363-1380, 2005.
  9. XindongWu , Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang ,Hiroshi Motoda ,Geoffrey J. McLachlan ,Angus Ng , Bing Liu ,Philip S. Yu , Zhi-Hua Zhou , Michael Steinbach ,David J. Hand , Dan Steinberg, "Top 10 algorithms in data mining", Knowledge and Information Systems Journal, Vol. 14, pp. 1-37, 2008.
  10. MacQueen, J. B. , Some Method for Classification and Analysis of Multivariate Observations, Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, (MSP'67), Berkeley, University of California Press, pp: 281-297, 1967.
  11. D. Lee, S. Baek, and K. Sung, "Modified k-means algorithm for vector quantizer design", IEEE Signal Processing Letters, Vol. 4, pp. 2–4, 1997.
  12. Kuldip K. Paliwal and V. Ramasubramanian, Comments on "Modified K-means Algorithm for Vector Quantizer Design", IEEE Transactions on Image Processing, Vol. 9 , No. 11, pp. 1964-1967, 2000.
  13. E. Forgy, "Cluster analysis of multivariate data: efficiency vs interpretability of classification", Biometrics, Vol. 21, pp. 768-769, 1965.
  14. Ball G. H. and Hall D. J. , "PROMENADE-an Online Pattern Recognition System", Stanford Research Institute Memo, Stanford University, 1967.
  15. Tou, J. and R. Gonzales, Pattern Recognition Principles, Addision-Wesley, Reading, MA. , pp: 377, 1977.
  16. Bradley, P. S. and U. M. Fayyad, " Refining initial points for K-means clustering", Proceedings of the 15th International Conference on Machine Learning (ICML'98), ACM Press, Morgan Kaufmann, San Francisco, pp. 91-99, 1998.
  17. Ali Ridho Barakbah and Yasushi Kiyoki, "A New Approach for Image Segmentation using Pillar-Kmeans Algorithm ", World Academy of Science, Engineering and Technology Journal , Vol. 59, pp. 23-28, 2009.
  18. Yanfeng Zhang, Xiaofei Xu and Yunming Ye, "An Agglomerative Fuzzy K-means clustering method with automatic selection of cluster number" ,2nd International Conference on Advanced Computer Control (ICACC), Vol. 2, pp. 32-38, 2010
  19. C. Huang and R. Harris, "A comparison of several codebook generation approaches", IEEE Transactions on Image Processing, Vol. 2 (1), pp. 108–112, 1993.
  20. H. H. Barret. Foundations of Image Science. John Wiley & Sons, New Jersey, U. K. , third edition, 2004.
  21. R. C. Gonzales and R. E. Woods. Digital Image Processing. Prentice Hall, second edition, 2002.

Keywords

Codebook, Covariance, Spectral Decomposition, Eigen value