Fuzzy entropy image segmentation based on particle swarm optimization

被引：26

作者：

Linyi Li a Deren Li b a School of Remote Sensing and Information Engineering Wuhan University Wuhan China b State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China ^{[430079
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机构：

来源：

Progress in Natural Science | 2008年 / 09期

关键词：

Image segmentation; Fuzzy entropy; Particle swarm optimization; Fuzzy parameter combinations; Fuzzy threshold;

D O I：

暂无

中图分类号：

O224 [最优化的数学理论];

学科分类号：

070105 ; 1201 ;

摘要：

Particle swarm optimization is a stochastic global optimization algorithm that is based on swarm intelligence.Because of its excellent performance, particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively.In this study, the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application.Then fuzzy entropy image segmentation based on particle swarm opti-mization is implemented and the proposed method obtains satisfactory results in the segmentation experiments.Compared with the exhaustive search method, particle swarm optimization can give the same optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments.Therefore, fuzzy entropy image segmentation based on particle swarm optimization is an eficient and promising segmentation method.

引用

页码：1167 / 1171

页数：5

共 5 条

[1] 基于二维灰度直方图的模糊熵分割方法 [J].