Simulated Annealing based Maximum Likelihood Clustering Algorithm for Image Segmentation
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    Abstract:

    Image segmentation can be regarded as the problem of two-class pattern classification. How to apply the maximum likelihood clustering algorithm to image segmentation is discussed in this paper. Simulated annealing technology is used to solve the problem of maximum likelihood clustering, which avoids the local optimal solution of iterative method. It shows better image segmentation effect than the famous Otsu algorithm and iterative method with less classification error than iterative method.

    Reference
    [1] Pal, N.R., Pal, S.K. A review on image segmentation techniques. Pattern Recognition, 1993,26(9):1277~1291.
    [2] Fu, K.S., Mui, J.K. A survey on image segmentation. Pattern Recognition, 1981,13(1):3~16.
    [3] Kunaga, Fu K. Introduction to Statistical Pattern Recognition. 2nd Edition, Boston: Academic Press, Inc., 1990.
    [4] Tekalp, A.M. Digital Video Processing. UK: Prentice-Hall, Inc., 1996.
    [5] 康立山,谢云,尤矢勇,等.非数值并行算法——模拟退火算法.北京:科学出版社,1997.
    [6] Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 1979,9(1):62~66.
    [7] Lee, Sang Uk, Chung, Seok Yook. A comparative performance study of several global thresholding techniques for segmentation. Computer Vision, Graphics, and Image Understanding, 1990,50(2):171~190.
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张引,潘云鹤.基于模拟退火的最大似然聚类图像分割算法.软件学报,2001,12(2):212-218

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History
  • Received:July 29,1999
  • Revised:October 28,1999
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