计算机科学 ›› 2014, Vol. 41 ›› Issue (8): 306-310.doi: 10.11896/j.issn.1002-137X.2014.08.065

• 图形图像与模式识别 • 上一篇    下一篇

基于改进鱼群算法的多阈值图像分割

崔丽群,宋晓,李鸿绪,张明杰   

  1. 辽宁工程技术大学软件学院 葫芦岛125105;辽宁工程技术大学软件学院 葫芦岛125105;辽宁工程技术大学软件学院 葫芦岛125105;辽宁工程技术大学软件学院 葫芦岛125105
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61172144),辽宁省教育厅项目(L2012113)资助

Multilevel Thresholding Image Segmentation Based on Improved Artificial Fish Swarm Algorithm

CUI Li-qun,SONG Xiao,LI Hong-xu and ZHANG Ming-jie   

  • Online:2018-11-14 Published:2018-11-14

摘要: 为了实现图像的有效分割,提出了一种基于改进鱼群算法的多阈值图像分割方法。引入领域搜索的思想对基本人工鱼群算法做了进一步改进;然后对最大熵函数进行全局优化,改进后的算法能够根据人工鱼的个体适应度大小和种群的分散程度自动调整鱼群控制参数,在保证群体多样性的同时加快了算法的收敛速度;最后得到分割图像的最佳阈值,克服了基本鱼群算法后期收敛性差、易陷入局部最优等问题。实验结果表明,所提算法能够获得较稳定、快速和准确的图像分割。

关键词: 鱼群算法,最大熵,多阈值,图像分割

Abstract: In order to realize the effective image segmentation,this paper proposed a new multilevel thresholding algorithm based on improved fish swarm algorithm for image segmentation.It introduces the idea of domain search for basic AFSA to make further improvements,and then realizes global optimization for the maximum entropy function.The improved algorithm could automatically adjust the parameters of fish swarm algorithm according to the individual fitness and the degree of dispersion of the population,while it ensures the diversity of the population and accelerates the convergence speed at the same time,and finally gets the vintage thresholds for image segmentation.It overcomes the shortcomings of basic AFSA including poor convergence,easy to fall into local optimum and other issues.Experimental results show that the proposed algorithm can achieve a more stable,fast and accurate image segmentation.

Key words: Artificial fish swarm algorithm(AFSA),Maximum entropy,Multilevel thresholding,Image segmentation

[1] 罗希平,田捷,诸葛婴,等.图像分割方法综述[J].模式识别与人工智能,1999,12(03):300-312
[2] Sezgin M,Sankur B.Servey over image thresholding techniques and quantitative performance evaluation[J].Journal of ElectronicImaging,2004,3(1):146-165
[3] 宋翠家,龙建忠,罗代升.基于遗传算法的模糊熵多阈值图像分割[J].仪器表学报,2004(z1):572-573
[4] Jiang Hua-wei,Yang Kai.Study of improved immune genetic algorithm for threshold image segmentation based on fuzzy maximum entropy[C]∥Proceedings of 2010 International Confe-rence on Computer,Mechatronics,Control and Electronic Engineering (CMCE 2010).2010
[5] 张国权.基于遗传算法的彩色图像多阈值分割方法研究[J].电子设计工程,2011,9(9):43-45
[6] 马磊,岳振军,王曙光.基于遗传算法的模糊多阈值图像分割方法[J].解放军理工大学学报:自然科学版,2003,4(6):37-40
[7] Ye Zhi-wei,Hu Zheng-bing,Wang Hua-min,et al.A ImageThresholding Method Based on Binary Coded Ant Colony Algorithm [C]∥2010 2nd International Workshop on Intelligent Systems and Applications (ISA).2010:1-4
[8] Wang Xiao-nian,Feng Yuan-jing,Feng Zu-ren.Ant colony optimization for image segmentation [C]∥Proceedings of 2005 International Conference on Machine Learning and Cybernetics.2005:5355-5360
[9] Gao Hao,Xu Wen-bo,Sun Jun,t al.Multilevel Thresholding for Image Segmentation Through an Improved Quantum-Behaved Particle Swarm Algorithm [J].IEEE Transactions on Instrumentation and Meas-urement,2010,9(4):934-946
[10] Cao Bin,Shen Xuan-jing,Qian Qing-ji.Application of two-order particle swarm optimization algorithm in image segmentation[C]∥2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design (CAIDCD).2010:749-752
[11] Zhang Xue-feng,Shang Jin-kui.Application of image segmentation algorithm based on particle swarm optimization and rough entropy standard[C]∥Control and Decision Conference.2009:2905-2909
[12] 武燕,张冰.基于改进粒子群算法的多阈值图像分割[J].微型电脑应用,2011,7(05):59-61,70
[13] 李厚强,刘政凯,詹曙.一种彩色纹理图像的分割方法[J].计算机学报,2001,4(9):965-971
[14] 史春奇,施智平,刘曦,等.基于自组织动态神经网络的图像分割[J].计算机研究与发展,2009,6(01):23-30
[15] 赵于前,李慧芬,王小芳.基于模拟退火算法的多阈值图像分割[J].计算机应用研究,2010,7(01):380-382
[16] 张引,潘云鹤.基于模拟退火的最大似然聚类图像分割算法[J].软件学报,2001,2(02):212-218
[17] Wang Qing-sheng,Zhang Yue-qin,et al.Improved genetic neural network for image segmentation [C]∥2011 IEEE 18Th International Conference on Industrial Engineering and Engineering Management (IE&EM).2011:1694-1698
[18] 纪则轩,陈强,孙权森,等.各向异性权重的模糊C均值聚类图像分割[J].计算机辅助设计与图形学学报,2009,1(10):1451-1459,1466
[19] 李晓磊.一种新型的智能优化方法-人工鱼群算法[D].杭州:浙江大学,2003
[20] 王培崇,雷凤君,钱旭.改进人工鱼群算法及其收敛性分析[J].科学技术与工程,2013,3(3):616-620
[21] 陈广洲,汪家权,李传军,等.一种改进的人工鱼群算法及其应用[J].系统工程,2009,7(12):105-110
[22] 刘彦君,江铭炎.自适应视野和步长的改进人工鱼群算法[J].计算机工程与应用,2009,5(25):35-37,47
[23] 朱命昊,厍向阳.求解旅行商问题的改进人工鱼群算法[J].计算机应用研究,2010,7(10):3734-3736
[24] 刘佳,刘丽娜,李靖,等.基于模拟退火算法的改进人工鱼群算法研究[J].计算机仿真,2011,8(10):195-198
[25] 王联国,洪毅,赵付青,等.一种改进的人工鱼群算法[J].计算机工程,2008,4(19):192-194
[26] 江铭炎,袁东风.人工鱼群算法及其应用[M].北京:科学出版社,2012
[27] Pun T.ANewMethod for Gray-Level Picture ThresholdingUsing the Entroy of the Histgran[J].Signal Processing,1980,7(2):223-237
[28] Kapur J,Sahop P,Wong A.ANewMethod for Grey-Level Pic-ture Thresholding Using the Entropy of the Histogram[J].Computer Vision Graphics and Image Processing,1985,4(29):210-239
[29] Kapur J N.Maximum Entropy Models in Science and Enginee-ring[M].New Delhi:Wiley Eastern,1989

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!