计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 223-225,243.doi: 10.3969/j.issn.1000-3428.2012.09.068

• 图形图像处理 • 上一篇    下一篇

基于人工蜂群优化的二维最大熵图像分割

阿里木?赛买提1,杜培军1,2,柳思聪1   

  1. (1. 中国矿业大学江苏省资源环境信息工程重点实验室,江苏 徐州 221116; 2. 南京大学卫星测绘技术与应用国家测绘地理信息局重点实验室,南京 210093)
  • 收稿日期:2012-01-13 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:阿里木?赛买提(1984-),男,硕士研究生,主研方向:遥感图像处理,机器学习;杜培军,教授、博士生导师;柳思聪,博士研究生
  • 基金项目:
    国家自然科学基金资助项目(40871195);江苏省自然科学基金资助项目(BK2010182)

Maximum 2D Entropy Image Segmentation Based on Artificial Bee Colony Optimization

Alim•Samat 1, DU Pei-jun 1,2, LIU Si-cong 1   

  1. (1. Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, 2. Key Laboratory for Satellite Surveying Technologies and Applications of State Administration of Surveying, Mapping and Geoinformation,
  • Received:2012-01-13 Online:2012-05-05 Published:2012-05-05

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

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