Color Image Segmentation Using a Model-Based Clustering and a MFA-EM Algorithm

  • Jong-Hyun Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


In this paper we present a statistical model-based approach to the color image segmentation. A novel deterministic annealing EM and mean field theory are used to estimate the posterior probability of each pixel and the parameters of the Gaussian mixture model which represents the multi-colored objects statistically. Image segmentation is carried out by clustering each pixel into the most probable component Gaussian. The experimental results show that the mean field annealing EM provides a global optimal solution for the ML parameter estimation and the real images are segmented efficiently using the estimates computed by the maximum entropy principle and men field theory.


Image Segmentation Gaussian Mixture Model Markov Random Field Block Image Maximum Entropy Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images,” IEEE Transactions on PAMI, vol. 6, pp. 721–741, 1984.zbMATHGoogle Scholar
  2. [2]
    J. Besag, “Towards Bayesian image analysis,” Journal of Applied Statistics, vol. 16, pp. 395–407, 1989.CrossRefGoogle Scholar
  3. [3]
    M. Saeed, W. C. Karl, T. Q. Nguyen, and H. R. Rabiee, “A new multiresolution algorithm for image segmentation,” International Conference on ASSP, vol. 5, pp. 2753–2733, 1998.Google Scholar
  4. [4]
    G. J. McLachlan, S.K. Nguyen, G.J. Galloway, and D. Wang, “Clustering of magnetic resonance images,” Technical Report, Department of Mathematics, University of Queensland, 1998.Google Scholar
  5. [5]
    T. Hofmann and J. M. Buhman, “Pairwise data clustering by deterministic annealing,” IEEE Transactions on PAMI, vol. 19, no. 1, pp. 1–13, 1998.Google Scholar
  6. [6]
    N. Ueda and R. Nakano, “Deterministic annealing EM algorithm”, Neural Networks, vol. 11, pp. 271–282, 1998.CrossRefGoogle Scholar
  7. [7]
    J. Zerubia and R. Chellappa, “Mean field annealing using compound Gauss Markov random fields,” IEEE Transactions on Neural Networks, vol. 4, no. 4, pp. 703–709, 1993.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jong-Hyun Park
    • 1
  1. 1.Department of Computer ScienceChonbuk National UniversityS. Korea

Personalised recommendations