计算机工程2012，Vol.38Issue(9)：223-225,243,4.DOI:10.3969/j.issn.1000-3428.2012.09.068
基于人工蜂群优化的二维最大熵图像分割
Maximum 2D Entropy Image Segmentation Based on Artificial Bee Colony Optimization
摘要
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.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Key words
image segmentation/ maximum 2D entropy/ artificial bee colony/ Partial Swarm Optimization(PSO)/ genetic algorithm/ artificial fish swarm/ genetic simulated annealing algorithm分类
信息技术与安全科学引用本文复制引用
阿里木·赛买提,杜培军,柳思聪..基于人工蜂群优化的二维最大熵图像分割[J].计算机工程,2012,38(9):223-225,243,4.基金项目
国家自然科学基金资助项目(40871195) （40871195）
江苏省自然科学基金资助项目(BK2010182) （BK2010182）
