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T. Uchiyama, M.A. Arbib, "Color Image Segmentation using Competitive Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 12, pp. 11971206, December, 1994.  
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@article{ 10.1109/34.387488, author = {T. Uchiyama and M.A. Arbib}, title = {Color Image Segmentation using Competitive Learning}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {12}, issn = {01628828}, year = {1994}, pages = {11971206}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.387488}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Color Image Segmentation using Competitive Learning IS  12 SN  01628828 SP1197 EP1206 EPD  11971206 A1  T. Uchiyama, A1  M.A. Arbib, PY  1994 KW  image colour analysis; image segmentation; unsupervised learning; least squares approximations; convergence of numerical methods; vector quantisation; color image segmentation; competitive learning; color space clustering; least sumofsquares criterion; convergence; optimum solution approximation; color scenes; efficiency; color coordinates VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sumofsquares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results.
[1] E. Riseman and M. Arbib, "Segmentation of static scenes,"Comput. Graphics Image Processing, vol. 6, pp. 221276, 1977.
[2] K. S. Fu and J. K. Mui, "A survey on image segmentation,"Pattern Recognition, vol. 13, pp. 316, 1981.
[3] R. M. Haralick and L. G. Shapiro, "Image segmentation techniques,"Comput. Vision, Graphics Image Processing, vol. 29, pp. 100132, 1985.
[4] Y. Fukada, "Spatial clustering procedures for region analysis,"Pattern Recognition, vol. 12, pp. 395403, 1980.
[5] S. L. Horowitz and T. Pavlidis, "Picture segmentation by a Tree Traversal Algorithm,"J. Assoc. Comput. Machinery, vol. 23, pp. 368388, 1976.
[6] R. Ohlander, K. Price, and D. R. Reddy, "Picture segmentation using a recursive region splitting method,"Comput. Graphics Image Processing, vol. 8, pp. 313333, 1978.
[7] Y. Ohta, T. Kanade, and T. Sakai, "Color information for region segmentation,"Comput. Vision, Graphics Image Processing, vol. 13, pp. 222241, 1980.
[8] H. Spath,Cluster Analysis Algorithms. Chichester: Ellis Horwood, 1980, pp. 5962.
[9] N. Otsu, "A threshold selection method from graylevel histograms,"IEEE Trans. Syst., Man, Cybern., vol. 9, pp. 6266, Jan. 1979.
[10] M. R. Anderberg,Cluster Analysis for Applications. New York: Academic, 1973.
[11] J. MacQueen, "Some methods for classification and analysis of multivariate observations," inProc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967, pp. 281297.
[12] G. H. Ball and D. J. Hall, "A clustering technique for summarizing multivariate data,"Behav. Sci., vol. 12, pp. 153155, 1967.
[13] G. B. Coleman and H. C. Andrews, "Image segmentation by clustering,"Proc. IEEE, vol. 67, pp. 773785, 1979.
[14] J. Bryant, "On the clustering of multidimensional pictorial data,"Pattern Recognition, vol. 11, pp. 115125, 1979.
[15] M. A. Ismail and M. S. Kamel, "Multidimensional data clustering utilizing hybrid search strategies,"Pattern Recognition, vol. 22, pp. 7589, 1989.
[16] Q. Zhang and R. D. Boyle, "A new clustering algorithm with multiple runs of iterative procedures,"Pattern Recognition, vol. 24, pp. 835848, 1991.
[17] Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design,"IEEE Trans. Commun., vol. COM28, pp. 8495, Jan. 1980.
[18] K. Fukunaga,Introduction to Statistical Pattern Recognition. New York: Academic, 1972.
[19] D. E. Rumelhart and D. Zipser, "Feature discovery by competitive learning,"Cognitive Sci., vol. 9, pp. 75112, 1985.
[20] T. Uchiyama, M Sakai, T. Saito, and T. Nakamura, "Optimum structure learning algorithms for competitive learning neural network," inProc. SPIE/SPSE's Electronic Imaging Science&Technology Symp., 1991.
[21] B. kosko, "Stochastic competitive learning,"IEEE Trans. Neural Netw., vol. 2, no. 5, pp. 522529, Sept. 1991.
[22] J. Hertz, A. Krogh, and R. G. Palmer.Introduction to the Theory of Neural Networks. Reading, MA: AddisonWesley, 1991.
[23] S. Z. Selim and M. A. Ismail, "KMeansType algorithms: A generalized convergence theorem and characterization of local oplimality,"IEEE Trans. Pattern Anal. Machine Intell., vol. 6, pp. 8187, Jan. 1984.
[24] B. Pratt,Digital Image Processing, 2nd edition, John Wiley and Sons, New York, 1992.
[25] S. Tominaga, "Color classification of natural color images,"Color Res. Appl., vol. 17, pp. 230239, 1992.