Image segmentation based on differential mutation bare bones particle swarm optimization and fuzzy entropy
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Abstract:
Basic bare bones particle swarm optimization (BBPSO) is easy to get stuck into local optima. Based on basic BBPSO, using the idea of mutation in differential evolution, a new algorithm named differential mutation bare bones particle swarm optimization (DMBBPSO) is proposed and combined with image fuzzy entropy to obtain a new segmentation algorithm based on DMBBPSO and fuzzy entropy for image segmentation. The proposed algorithm uses DMEBBPSO to explore fuzzy parameters of maximum fuzzy entropy and gets the image segmentation threshold. According to the experiment results, compared with other two algorithms, the proposed algorithm shows better segmentation performance and very low time cost. It can be used to real time and precision measure coal dust image.
