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. © Springer-Verlag; 2003.
CITATION STYLE
Park, J. H. (2003). Color image segmentation using a model-based clustering and a MFA-EM algorithm. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2749, 934–941. https://doi.org/10.1007/3-540-45103-x_123
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