A genetic algorithm for MRF-based segmentation of multi-spectral textured images

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Title:	A genetic algorithm for MRF-based segmentation of multi-spectral textured images
Authors:	Tseng,DC;Lai,CC
Contributors:	資訊工程研究所
Keywords:	MARKOV RANDOM-FIELD;UNSUPERVISED SEGMENTATION;COLOR IMAGES;CLASSIFICATION;RELAXATION;MODELS
Date:	1999
Issue Date:	2010-06-29 20:14:36 (UTC+8)
Publisher:	中央大學
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. Tn this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multispectral 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. (C) 1999 Elsevier Science B.V. All rights reserved.
Relation:	PATTERN RECOGNITION LETTERS
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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