A genetic algorithm for MRF-based segmentation of multi-spectral textured images |
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Authors: | Din-Chang Tseng Chih-Ching Lai |
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Affiliation: | Institute of Computer Science and Information Engineering, National Central University, Chung-li 320, Taiwan, ROC |
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Abstract: | A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. In this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multi-spectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images. |
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Keywords: | Unsupervised texture segmentation Markov random field Genetic algorithm Multi-spectral remote-sensing images |
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