Fuzzy entropy image segmentation based on particle swarm optimization |
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Authors: | Linyi Li Deren Li |
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Affiliation: | 1. School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China 2. State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China |
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Abstract: | 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 optimization 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 efficient and promising segmentation method. |
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Keywords: | Image segmentation Fuzzy entropy Particle swarm optimizafion Fuzzy parameter combinations Fuzzy threshold |
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