Multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm

doi:10.3778/j.issn.1002-8331.2010.10.055

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (10): 175-176.DOI: 10.3778/j.issn.1002-8331.2010.10.055

• 图形、图像、模式识别 • Previous Articles Next Articles

Multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm

JIANG Yan-hui，LI Feng

Computer and Communication Engineering Institute，Changsha University of Science & Technology，Changsha 410076，China

Received:2008-09-25 Revised:2008-12-26 Online:2010-04-01 Published:2010-04-01
Contact: JIANG Yan-hui

基于混沌粒子群算法的多阈值图像分割

蒋艳会，李峰

长沙理工大学计算机与通信工程学院，长沙 410076

通讯作者: 蒋艳会

Abstract

Abstract: Due to the problems of the single threshold image segmentation method isn’t ideal，the particle swarm optimization algorithm is easy to fall into local optimum，and the speed is slow，a multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm is proposed to solve the optimization problems.By using randomicity，ergodic and initial value sensitivity of chaos，chaotic particle swarm optimization algorithm is combined with multi-threshold method.The experimental result indicates multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm reduces the searching time，and the operating time doesn’t significantly enhance with the increase number of threshold.

Key words: image segmentation, particle swarm optimization algorithm, multi-threshold, chaos

摘要： 针对单阈值图像分割方法在求取比较复杂的图像时效果不理想及粒子群算法容易陷入局部最优且速度较慢等等问题，提出了基于混沌粒子群优化算法的多阈值图像分割方法。该方法利用混沌运动随机性、遍历性和初值敏感性，将混沌粒子群优化算法与多阈值法相结合作全局搜索，实验结果表明了基于混沌粒子群优化算法的多阈值图像分割法用于阈值寻优减少了搜索时间，并且运行时间不随阈值数目的增加而显著增加。

关键词: 图像分割, 粒子群优化算法, 多阈值, 混沌

CLC Number:

TP391.4

JIANG Yan-hui，LI Feng. Multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2010, 46(10): 175-176.

蒋艳会，李峰. 基于混沌粒子群算法的多阈值图像分割[J]. 计算机工程与应用, 2010, 46(10): 175-176.

[1]	ZHAO Yang, ZHANG Junhua. Multi-scale Feature Fusion Method for Spinal X-Ray Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 214-219.
[2]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[3]	DONG Peng, ZHOU Feng, ZHAO Congcong, WANG Yafei, MI Zetian, FU Xianping. Automatic Measurement of Underwater Sea Cucumber Size Based on Binocular Vision [J]. Computer Engineering and Applications, 2021, 57(8): 271-278.
[4]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[5]	PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie. Survey of Medical Image Segmentation Algorithm in Deep Learning [J]. Computer Engineering and Applications, 2021, 57(3): 44-57.
[6]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[7]	LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie. COVID-19 Medical Imaging Dataset and Research Progress [J]. Computer Engineering and Applications, 2021, 57(22): 15-27.
[8]	QI Xiaoxiang, LI Min, ZHU Ying, SONG Yu, DU Weidong. Adaptive Region Segmentation of SAR Image Based on Edge Detection [J]. Computer Engineering and Applications, 2021, 57(22): 232-240.
[9]	ZHOU Shaoguang, WU Hao, ZHAO Chanjuan, CHEN Renxi. Transfer Learning for Hyperspectral Image Classification Using Homogeneous Area Characteristics [J]. Computer Engineering and Applications, 2021, 57(21): 224-233.
[10]	WANG Hairui, XIANYU Jianchuan. Application of Distributed Generation Configuration Based on Improved Sparrow Search Algorithm [J]. Computer Engineering and Applications, 2021, 57(20): 245-252.
[11]	LI Jiasheng, GUO Shuxu, ZHANG Lei, ZHENG Shuang, ZHANG Huimao, QIU Yunhai, LI Xueyan. Research of Segmentation and Detection of Liver Metastases from CT Images Based on Deep Network [J]. Computer Engineering and Applications, 2021, 57(2): 191-197.
[12]	DING Cheng, WENG Liguo, XIA Min, CUI Yichen, QIAN Junhao, LIU Jia. Multi-attention Mechanism Network Satellite Image Segmentation Algorithm [J]. Computer Engineering and Applications, 2021, 57(2): 223-229.
[13]	LI Zhenqiang, WANG Shucai, ZHAO Shida, BAI Yu. Cutting Methods of Sheep’s Trunk Based on Improved DeepLabv3+ and XGBoost [J]. Computer Engineering and Applications, 2021, 57(18): 263-269.
[14]	SUN Weijie, YANG Jun. Research on Remote Sensing Image Segmentation Based on Improved Simple Non-iterative Clustering [J]. Computer Engineering and Applications, 2021, 57(13): 185-192.
[15]	LIAO Liefa, YANG Hong. Review of Beetle Antennae Search [J]. Computer Engineering and Applications, 2021, 57(12): 54-64.

Multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm

基于混沌粒子群算法的多阈值图像分割

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics