关键词: 广义模糊熵, 自适应差分进化, 阈值分割, 模糊集, 补函数

Abstract: Aiming at the problem that the parameters of image threshold segmentation for generalized fuzzy entropy can not be selected automatically, the image threshold segmentation method for generalized fuzzy entropy based on adaptive differential evolution is proposed. The optimal parameters for threshold segmentation of generalized fuzzy entropy are selected by using the adaptive differential evolution algorithm as the optimization tool, and the adaptive mutation operator and the crossover probability adaptive function are introduced to control the optimization process. The threshold of the image is obtained by adding the parameters to the complement function of the generalized fuzzy entropy, and then the optimal segmentation is obtained. In order to verify the effectiveness and feasibility of the proposed algorithm, this paper compares it with the image threshold segmentation algorithm for fuzzy entropy based on the basic image quality evaluation criteria and the image threshold segmentation algorithm for generalized fuzzy entropy based on the particle swarm optimization. Experiments show that, for different details of the picture, the segmentation results of the algorithm have less background information in most cases, its target information is more clearly, and it takes less time. In addition, its segmentation is more stable and effective.

Key words: generalized fuzzy entropy, Adaptive Differential Evolution（ADE）, threshold segmentation, fuzzy set, complement function