摘要： 针对二维最大熵图像分割方法计算量大的问题，提出基于人工蜂群优化的二维最大熵图像分割算法。利用人工蜂群优化算法收敛快、避免局部最优、控制参数少等优点，将二维最大熵法最佳二维阈值视为最佳蜜源，实现基于人工蜂群优化的二维最大熵图像分割。实验结果表明，该方法的收敛速度较快、抗噪性较强。

关键词: 图像分割, 二维最大熵, 人工蜂群, 粒子群优化, 遗传算法, 人工鱼群, 遗传模拟退火算法

Abstract: Aiming at the problem of large computing in maximum 2D entropy based image segmentation method, this paper proposes a maximum 2D entropy image segmentation algorithm based on artificial bee colony optimization. Artificial bee colony algorithm has certain advantage in convergence speed, prevents local optimization, and has few control parameters. Using these advantages, the best 2D threshold of maximum 2D entropy method is considered as nectar, and artificial bee colony optimized maximum 2D entropy method is used to segment images. Experimental result shows that, compared with other methods, constriction of this method is quicker, stability is better and resistance to the noise is stronger.

Key words: image segmentation, maximum 2D entropy, artificial bee colony, Partial Swarm Optimization(PSO), genetic algorithm, artificial fish swarm, genetic simulated annealing algorithm

中图分类号: 

• TP911.73