Color image segmentation using a model-based clustering and a MFA-EM algorithm

  • Authors:

  • Jong-Hyun Park

  • Affiliations:

  • Department of Computer Science, Chonbuk National University, S. Korea

  • Venue:
  • SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
  • Year:
  • 2003

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Abstract

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.