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Reseach Article

Segmentation by Incremental Clustering

by Dao Nam Anh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 12
Year of Publication: 2015
Authors: Dao Nam Anh
10.5120/19591-1360

Dao Nam Anh . Segmentation by Incremental Clustering. International Journal of Computer Applications. 111, 12 ( February 2015), 23-30. DOI=10.5120/19591-1360

@article{ 10.5120/19591-1360,
author = { Dao Nam Anh },
title = { Segmentation by Incremental Clustering },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 12 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number12/19591-1360/ },
doi = { 10.5120/19591-1360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:42.923428+05:30
%A Dao Nam Anh
%T Segmentation by Incremental Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 12
%P 23-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A method for unsupervised segmentation by incremental clustering is introduced. Inspired by incremental approach and correlation clustering, clusters are added and refined during segmentation process. Correlation clustering is to keep away from pre-definition for number of clusters and incremental approach is to avoid re-clustering that is needed in iterative methods. The Gaussian spatial kernel is involved like a part of similarity function to cover local image structure. Cluster representative is updated efficiently to satisfy the old and new similarity constraints rather than re-clustering the entire image. Experimental results are discussed and show that the algorithm requires reasonable computational complexity while gaining a comparable or better segmentation quality than standard methods.

References
  1. J. MacQueen, Some Methods for Classification and Analysis of Multivariate Data, Proc. 5th Berkeley Symposium on Probability and Statistics, May 1967.
  2. J. A. Hartigan and M. A. Wong, Algorithm AS 136: A k-means clustering algorithm, Applied Statistics, vol. 28, no. 1, pp. 100–108, 1979.
  3. R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, Wiley, Toronto, 1973.
  4. Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN 0-387-31073-8.
  5. Cheng, Yizong (August 1995). Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE) 17 (8): 790–799. doi:10. 1109/34. 400568.
  6. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. , 24:603–619, 2002.
  7. D. M. Greig, B. T. Porteous and A. H. Seheult (1989), Exact maximum a posteriori estimation for binary images, Journal of the Royal Statistical Society Series B, 51, 271–279.
  8. Dempster, A. P. ; Laird, N. M. ; Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39 (1): 1–38. JSTOR 2984875. MR 0501537.
  9. C. F. Jeff Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, Vol. 11, No. 1 1983, pp. 95-103.
  10. Vaida F. Parameter convergence for EM and MM algorithms. Statistica Sinica 2005; 15:831-840.
  11. Nikhil Bansal, Avrim Blum, and Shuchi Chawla. Correlation clustering. In Proceedings of the 43rd Annual IEEE SFCS, pp 238250, 2002.
  12. Becker, H. A survey of correlation clustering. Available online at www. cs. columbia. edu/~hila/clustering. pdf, 2005.
  13. Ester M. , Kriegel H. -P. , Sander J. , Xu X. : Incremental Clustering for Mining in a Data Warehousing Environment, Proc. 24th Int. Conf. on Very Large Databases (VLDB '98), New York City, NY, 1998, pp. 323-333.
  14. R. Sibson (1973). SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal (British Computer Society) 16 (1): 30–34. doi:10. 1093/comjnl/16. 1. 30.
  15. M. Charikar, C. Chekuri, T. Feder, and R. Motwani. Incremental clustering and dynamic information retrieval. In The 29th annual ACM symposium on Theory of computing, pp 626–635, 1997.
  16. M. Lindenbaum, M. Fischer, and A. M. Bruckstein, On Gabor's contribution to image enhancement, Pattern Recognition, 27 (1994), pp. 1–8.
  17. Dunn, J. C. : A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well Separated Clusters. Jour. of Cybernetics, Vol. 3, 1974, pp. 32–57.
  18. C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In Proc. of the Sixth International Conference on Computer Vision, India, 1998.
  19. Kai X, Jianli L, Shuangjiu X, Haibing G, Fang F and A E Hassanien, Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation, (IJFSA), Vol3, Issue 4. 2013. 13 pp.
  20. Guillaume B, M Verleysen, John A. Lee, Incremental feature computation and classification for image segmentation, ESANN 2012, ISBN 978-2-87419-049-0.
  21. Xiaoke S, Yang L, Renxia W, and Yuming Q, A Fast Incremental Clustering Algorithm, Proceedings of the 2009 (ISIP'09) ISBN 978-952-5726-02-2 (Print), 978-952-5726-03-9, 2009, pp. 175-178.
  22. Rashedi, E. ; Nezamabadi-Pour, H. A Stochastic Gravitational Approach To Color Image Segmentation By Considering Spatial Information Engineering Applications Of Artificial Intelligence, V26, N4, 2013.
  23. Aaron, B. ;Tamir, D. E. ; Rishe, N. D. ; Kandel, A. Dynamic Incremental k-Means Clustering, (CSCI), 2014 (Vol:1), DOI: 10. 1109/CSCI. 2014. 60.
  24. Pham, Duc Truong, Dimov, Stefan Simeonov and Nguyen, C. D. 2004. An incremental k-Means algorithm. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218 (7), pp. 783-795. 10. 1243/0954406041319509.
  25. Sanjay Chakraborty, N. K. Nagwani, Analysis and Study of Incremental K-Means Clustering Algorithm, High Performance Architecture and Grid Computing Communications in Computer and Information Science Vol 169, 2011, pp 338-341.
  26. Meng, Xiao-Li; Rubin, Donald B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80 (2): 267–278. doi:10. 1093/biomet/80. 2. 267. MR 1243503.
  27. Hunter DR and Lange K (2004), A Tutorial on MM Algorithms, The American Statistician, 58: 30-37.
  28. Chris Fraley, Adrian E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis, The Computer Journal, 20:270–281, 1998.
  29. P. Lambert, H. Grecu, A quick and coarse color image segmentation, DOI: 10. 1109/ ICIP. 2003. 1247125 Conference: Image Processing, 2003. Vol. 1.
  30. D. Martin and C. Fowlkes and D. Tal and J. Malik, A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, Proc. 8th Int'l Conf. Computer Vision, 2001, vol. 2, pp 416—423.
  31. W. M. Rand (1971). Objective criteria for the evaluation of clustering methods. Journal ASA 66 (336): 846–850. doi:10. 2307/2284239. JSTOR 2284239.
  32. Ranjith Unnikrishnan, Caroline Pantofaru and Martial hebert, Measures of Similarity Proceedings of the Seventh IEEE Workshop on Applications of Computer Vision, 2005, pp. 394.
Index Terms

Computer Science
Information Sciences

Keywords

Incremental Clustering Correlation Clustering Unsupervised Segmentation