Segmentation of mrf based image using hierarchical genetic algorithm
In this paper, a segmentation of an Markov Random Field based image is proposed. We use a hierarchical image model consists of color, blurring and noise parameters and define an energy function for proper image segmentation criterion. In general, it is not easy to search optimal parameter values which optimize the given energy function. To search optimal parameter values effectively, Hierarchical Genetic Algorithm is used and the experimental results show the effectiveness of the proposed method.
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- F. R. Hansen and H. Elliott, “Image Segmentation using simple Markov field models,” Computer Graphics Image Processing, vol. 20, pp. 101–132,1982Google Scholar
- F. Cohen and Z. Fan, “Maximum likelihood unsupervised texture image segmentation”, CVGIP, vol. 54, no. 3, pp. 239–251, 1992.Google Scholar
- I. Y. Kim and H. S. Yang, “Efficient Image Labeling based on Markov Random Field and Error Backpropagation Network”, Pattern Recognition, vol. 26, no. 11, pp. 1695–1707,1993.Google Scholar
- R. G. Gonzalez and R. E. Woods, Digital image processing, Addison-Wesley Publish Co. 1992.Google Scholar
- J. W. Kim and B. P. Zeiger, “Hierarchical Distributed Genetic Algorithms: A Fuzzy Logic Controller Design Application”, IEEE EXPERT, vol. 11, no. 3, 1996.Google Scholar
- N. R. Pal and S. k. Pal, “A Revies On Image Segmentation Techniques,” Pattern Recognition, vol.26, no.9, pp 1277–1294, 1983Google Scholar
- P. Andrey and P. Tarroux, “Unsupervised Image Segmentation using A Distributed Genetic Algorithm”, Pattern Recognition, vol. 27, no. 5, pp. 659–673, 1994.Google Scholar
- S. Gemam and D. Geman, “Stochastic relaxation, Gibbs distributions, and the baysian restoration of images”, IEEE Trans. PAMI, vol PAMI-6, no. 6, pp. 721–741, 1984.Google Scholar
- D. E. Golberg, Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley, Reading, Massachusetts 1989.Google Scholar