基于广义反向粒子群与引力搜索混合算法的多阈值图像分割

巢渊; 戴敏; 陈恺; 陈平; 张志胜

doi:10.3788/OPE.20152303.0879

您当前的位置：

首页 >

文章列表页 >

基于广义反向粒子群与引力搜索混合算法的多阈值图像分割

信息科学 | 更新时间：2020-08-13

- 基于广义反向粒子群与引力搜索混合算法的多阈值图像分割
- Image segmentation of multilevel threshold using hybrid PSOGSA with generalized opposition-based learning
- 光学精密工程 2015年23卷第3期页码：879-886
- 作者机构：
  
  东南大学机械工程学院,江苏南京,211189
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51275090)();江苏省科技成果转化基金资助项目(No.BA2010093)();中央高校基本科研业务费专项资金资助项目(No.K
- DOI：10.3788/OPE.20152303.0879
  中图分类号： TP391.4
- 收稿日期：2014-11-04，
  
  修回日期：2014-12-22，
  
  纸质出版日期：2015-03-25
- 稿件说明：
移动端阅览
巢渊, 戴敏, 陈恺等. 基于广义反向粒子群与引力搜索混合算法的多阈值图像分割[J]. 光学精密工程, 2015,23(3): 879-886

CHAO Yuan, DAI Min, CHEN Kai etc. Image segmentation of multilevel threshold using hybrid PSOGSA with generalized opposition-based learning[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 879-886
巢渊, 戴敏, 陈恺等. 基于广义反向粒子群与引力搜索混合算法的多阈值图像分割[J]. 光学精密工程, 2015,23(3): 879-886 DOI： 10.3788/OPE.20152303.0879.

CHAO Yuan, DAI Min, CHEN Kai etc. Image segmentation of multilevel threshold using hybrid PSOGSA with generalized opposition-based learning[J]. Editorial Office of Optics and Precision Engineering, 2015,23(3): 879-886 DOI： 10.3788/OPE.20152303.0879.

摘要

提出了基于粒子群优化(PSO)与引力搜索(GSA)混合算法(PSOGSA)的多阈值图像分割方法来解决图像阈值搜寻过程中单一优化算法局部搜索能力不强的问题。提出了图像阈值分割领域中的广义反向学习策略

在阈值寻优过程中提高群体多样性

增强了全局搜索能力;采用了全局最优解的正态变异策略

扩展了全局最优的搜索区域

避免了算法的早熟收敛。在此基础上

实现了基于广义反向粒子群与引力搜索混合算法的多阈值图像分割方法。最后

使用本方法对复杂多目标图像进行了多阈值分割实验

并与引力搜索算法和萤火虫算法进行了比较。实验结果表明

本文方法的分割精度优于引力搜索算法与萤火虫算法

其分割目标函数值在连续运行时的标准差降低了90%以上

是一种精度高、稳定性强的多阈值图像分割方法。

Abstract

A multilevel threshold image segmentation method based on hybrid Particle Swarm Optimization (PSO) and Gravitation Search Algorithm (GSA) was proposed to solve the weakness that a single algorithm in image segmentation has a lower local searching ability. A strategy of generalized opposition-based learning in image segmentation was proposed to improve the population diversity and to strengthen the global searching ability in optimizing processing. The normal mutation strategy on the best particle was conducted to extend the searching space and to avoid the premature convergence of the algorithm. Then

the multilevel threshold image segmentation method of hybrid PSOGSA with generalized opposition-based learning was implemented. Finally

complex image segmentation experiments were processed by proposed method and the results were compared with those of multilevel threshold segmentation methods of GSA and Firefly Algorithm (FA). Experimental results show the proposed method possesses a higher accuracy in multilevel threshold segmentation and the standard deviation of best objective values in continuous operation has decreased by up to 90%. Therefore

the image segmentation method of multilevel threshold using the hybrid PSOGSA with generalized opposition-based learning can be accurately and stably used in multilevel threshold image segmentation.

关键词

Keywords

references

何志勇, 孙立宁, 黄伟国, 等. 基于Otsu准则和直线截距直方图的阈值分割 [J]. 光学精密工程, 2012, 20(10):2315-2323. HE ZH Y, SUN L N, HUANG W G, et al.. Thresholding segmentation algorithm based on Otsu criterion and line intercept histogram [J]. Opt. Precision Eng. , 2012, 20(10):2315-2323. (in Chinese)

XIA D X, LI C G, YANG SH. Fast threshold selection algorithm of infrared human images based on two-dimensional fuzzy tsallis entropy [J]. Mathematical Problems in Engineering, 2014.

吴一全, 孟天亮, 王凯. 基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取 [J]. 光学精密工程, 2014, 22(1):235-243. WU Y Q, MENG T L, WANG K. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization [J]. Opt. Precision Eng., 2014, 22(1):235-243. (in Chinese)

陈恺, 陈芳, 戴敏, 等. 基于萤火虫算法的二维熵多阈值快速图像分割 [J]. 光学精密工程, 2014, 22(2):517-523. CHEN K, CHEN F, DAI M, et al.. Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm [J]. Opt. Precision Eng. , 2014, 22(2):517-523. (in Chinese)

AGRAWAL S, PANDA R, BHUYAN S, et al.. Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm [J]. Swarm and Evolutionary Computation, 2013, 11:16-30.

LAI X SH, ZHANG M Y. An efficient ensemble of GA and PSO for real function optimization [C]. 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, Beijing, China, 2009.

WANG G G, GANDOMI A H, ALAVI A H, et al.. Hybrid krill herd algorithm with differential evolution for global numerical optimization [J]. Neural Computing and Applications, 2014, 25(2):297-308.

MIRJALILI S, HASHIM S Z M. A new hybrid PSOGSA algorithm for function optimization [C]. International Conference on Computer and Information Application(ICCIA) 2010, Tianjin, China, 2010.

KENNEDY J, EBERHART R. Particle swarm optimization [C]. IEEE International Conference on Neural Networks, Part 4 (of 6), Perth, 1995.

RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA:A Gravitational search algorithm [J]. Information Sciences, 2009, 179(13):2232-2248.

SATHYA P D, KAYALYIZHI R. Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm [J]. Neurocomputing, 2011, 74(14-15):2299-2313.

TIZHOOSH HR. Opposition-based learning:A new scheme for machine intelligence [C]. In International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005, November 28, 2005-November 30, 2005, Vienna, Austria, 2005.

WANG H, WU Z, RAHNAMAYAN S, et al.. Enhancing particle swarm optimization using generalized opposition-based learning [J]. Information Sciences, 2011, 181(20):4699-4714.

浏览量

468

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于萤火虫算法的二维熵多阈值快速图像分割

融合条形注意力机制的电芯极片计算机断层图像分割

增强型金枪鱼群优化指数熵的砂粒显微图像分割

语义流引导采样结合注意力机制的脑肿瘤图像分割

混沌SSA优化多重熵阈值的骨料图像自动分割