A New Image Threshold Segmentation based on Fuzzy Entropy and Improved Intelligent Optimization Algorithm

Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the maximum entropy criterion, the improved particle swarm optimization algorithm based on chaos bee colony is used to determine the optimal parameters of membership function to automatically determine the optimal threshold segmentation. The experiment results show that proposed algorithm based on fuzzy entropy and chaos bee colony particle swarm optimization has good performance.