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

被引：32

作者：

Tseng, DC ^{[1
]}

Lai, CC ^{[1
]}

机构：

[1] Natl Cent Univ, Inst Comp Sci & Informat Engn, Chungli 320, Taiwan

来源：

PATTERN RECOGNITION LETTERS | 1999年 / 20卷 / 14期

关键词：

unsupervised texture segmentation; Markov random field; genetic algorithm; multi-spectral remote-sensing images;

D O I：

10.1016/S0167-8655(99)00117-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1499 / 1510

页数：12

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