基于自适应CPSO算法的二维模糊熵图像阈值分割

2D Fuzzy Entropy Image Threshold Segmentation Method Based on CPSO

由于PSO算法会出现虚假收敛或者早熟等现象,提出了一种自适应混沌粒子群算法(ACPSO)及其在图像分割中的应用。首先提出了一种改进的自适应粒子群优化算法(IAPSO)。然后在IAPSO的基础上,加入了混沌优化方法,用混沌变量来初始化粒子的位置和速度,并用新的无限折叠混沌映射对算法进行混沌变异,从当前群体中择优选择部分粒子进行混沌优化。最后将ACPSO算法应用到图像分割中。通过与最大模糊Shannon熵阈值分割法、基于基本PSO的最大模糊Shannon熵阈值分割法进行对比,验证了基于自适应CPSO算法的二维模糊熵阈值图像分割方法的性能更好。

Because PSO algorithm is occurrence of false convergence or precocious, an adaptive chaotic particle swarm optirnization（ACPSO） and its application to image segmentation were proposed. First, an improved adaptive particle swarm optimization algorithm（IAPSO） was proposed. Second, the chaos optimization algorithm was joined into the IAPSO. The chaotic variable was used to initialize the position and velocity of the particles. The new Infinite Collapses chaos chaotic mutation mapping algorithm was used to choose the best part of particles from the current population cha- os optimization. Finally, ACPSO algorithm was applied to image segmentation. The maximum fuzzy Shannon entropy threshold segmentation method was compared with maximum fuzzy Shannon entropy threshold segmentation method based on the basic PSO. The result indicates that the fuzzy entropy threshold image segmentation method based on the algorithm for the adaptive CPSO has better performance.