An MRF-Based Texture Segmentation Using Genetic Algorithm


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 5, No. 10, pp. 2713-2724, Oct. 1998
10.3745/KIPSTE.1998.5.10.2713,   PDF Download:

Abstract

This paper proposes a new method for the parameter estimation in Markov Random Field(MRF) model of textured color images. The MRF models allow an image region to be described using a finite number of parameters that characterize spatial interactions within and between bands of a color image. An important problem is estimation of the parameters since the random field model-based textured color image is the mostly parametric model specified by a number of parameters. To deal with the problem, we use a genetic algorithm. A test with color images of natural scenes to verify the validity of the proposed method proves that the method is not affected by the size of the image and shows well-segmented images.


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Cite this article
[IEEE Style]
L. K. Mi, K. S. Kyoon, K. H. Joon, "An MRF-Based Texture Segmentation Using Genetic Algorithm," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 5, no. 10, pp. 2713-2724, 1998. DOI: 10.3745/KIPSTE.1998.5.10.2713.

[ACM Style]
Lee Kyung Mi, Kim Sang Kyoon, and Kim Hang Joon. 1998. An MRF-Based Texture Segmentation Using Genetic Algorithm. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 5, 10, (1998), 2713-2724. DOI: 10.3745/KIPSTE.1998.5.10.2713.