The week's pick
Reseach Article
Novel K-means Algorithm for Compressing Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 18 - Number 8 |
| Year of Publication: 2011 |
| Authors: K. Somasundaram, M. Mary Shanthi Rani |
10.5120/2306-1764
|
K. Somasundaram, M. Mary Shanthi Rani . Novel K-means Algorithm for Compressing Images. International Journal of Computer Applications. 18, 8 ( March 2011), 9-13. DOI=10.5120/2306-1764
Abstract
Our proposed method is a two phase scheme that enhances the performance of K-means vector quantization algorithm for compressing images. In the proposed method, we have explored the possibility of application of statistical parameters for choosing the initial seeds for K-means algorithm. The selection of initial seeds depends on the statistical features of input data set. The novelty in our approach is the judicious selection of initial seeds based on variance, mean, median and mode parameters. Considering mode value of each dimension of the data adds uniqueness to our method. Our approach shows better performance yielding good PSNR and variable bit rate at a very low time complexity. This method is best suited for online web applications that involve massive and rapid image and video transmission.
References
- Lloyd, S.P., 1982. Least square quantization in PCM, IEEE Trans. Inform. Theory, vol 28: pp 129-136.
- Ball, G.H. and D.J. Hall, 1967. PROMENADE-an online pattern recognition system, Stanford Research Inst. Memo, Stanford University
- Astrahan, M.M., 1970. Speech Analysis by Clustering, or the Hyperphoneme Method, Stanford A. I. Project Memo, Stanford University .
- Kaufman, L. and Rousseeuw, 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, ISBN: 0471878766, pp: 342.
- Gersho, A. and R.M. Gray, 1992. Vector Quantization and Signal Compression, Kluwer Academic, Boston, ISBN: 0792391810, pp: 761.
- Katsavounidis, I., C.C.J. Kuo and Z. Zhen, 1994. A new initialization technique for generalized Lloyd iteration. IEEE. Sig. Process. Letters, pp 144-146
- Fayyad, U.M., G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, 1996. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, ISBN: 0262560976, pp: 611.
- Bradley, P.S. and U.M. Fayyad, 1998. Refining initial points for K-means clustering. Proceeding of the 15th International Conference on Machine Learning (ICML’98), July 24-27, ACM Press, Morgan Kaufmann, San Francisco, pp: 91-99.
- Fahim, A.M., A.M. Salem, F.A. Torkey and M. Ramadan, 2006. An efficient enhanced k-means clustering algorithm. J. Zhejiang Univ. Sci. A., 7: pp:1626-1633.
- Deelers, S. and S. Auwatanamongkol, 2007. Enhancing K-means algorithm with initial cluster centers derived from data partitioning along the data axis with the highest variance. Proc. World Acad. Sci. Eng. Technol., 26: pp: 323-328.
- Arthur, D. and S. Vassilvitskii, 2007. k-means++: The advantages of careful seeding. Proceeding of the 18th Annual ACM-SIAM Symposium of Discrete Analysis, Jan. 7-9, ACM Press, New Orleans, Louisiana, pp: 1027-1035.
- K. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao and G.R. Sridhar, Single Pass Seed Selection Algorithm for k-Means, Journal of Computer Science 6 (1): pp: 60-66.
- Kekre, H.B. Sarode, T.K. Thadomal Shahani , An Efficient Fast Algorithm to Generate Codebook for Vector Quantization , proceedings of International Conference on Emerging Trends in Engineering and Technology, pp.62-67.
- Tou, J. and R. Gonzales, 1977. Pattern Recognition Principles. Addision-Wesley, Reading, MA., ISBN: 0201075873, pp: 377
- Feng Wu, Xiaoyan Sun, Image Compression by Visual Pattern Vector Quantization (VPVQ) , Data Compression conference, 1068-0314/08 © 2008 .
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