An Improved Image Segmentation Method Based on Maximum Fuzzy Entropy and Quantum Genetic Algorithm

Abstract

In order to improve the speed of image segmentation, this paper proposes a hybrid algorithm combined maximum fuzzy entropy and quantum genetic algorithm. Based on the fuzzy set theory, the pixels in the original image are divided into three fuzzy sets: dark, gray and bright, according to the gray value of the pixel. And the maximum fuzzy entropy criterion is used to find the optimal combination of fuzzy parameters and realize image segmentation. Due to the high computational complexity of the exhaustive method to determine the optimal parameters combination, the quantum genetic algorithm is used to determine the optimal threshold. The experimental result shows that the proposed algorithm runs faster than algorithm combined maximum fuzzy entropy and genetic algorithm.