基于K-均值改进蚁群优化的彩色图像边缘检测算法

doi:10.3969/j.issn.1006-2475.2016.09.013

计算机与现代化

基于K-均值改进蚁群优化的彩色图像边缘检测算法

（1.江西省计算技术研究所,江西南昌330003;2.江西省软件工程技术研究中心,江西南昌330003）

收稿日期:2016-04-18 出版日期:2016-09-12 发布日期:2016-09-13
作者简介:刘波平（1963-），男，江西吉安人，江西省计算技术研究所和江西省软件工程技术研究中心教授级高级工程师，博士，研究方向：信息管理，检测分析；孟莎莎（1984-），女，山西大同人，工程师，硕士，研究方向：图像处理，自动控制。
基金资助:
国家科技支撑计划项目（2014BAD10B00,2014BAD10B05）

Color Image Edge Detection Algorithm Based on K-means and Improved Ant Colony Optimization

(1. Jiangxi Institute of Computing Technology, Nanchang 330003, China; 2. Software Engineering and Technical Research Center of Jiangxi Province, Nanchang 330003, China)

Received:2016-04-18 Online:2016-09-12 Published:2016-09-13

摘要/Abstract

摘要： 为提高图像边缘检测的精度，提出一种基于K-均值改进蚁群优化（ACO）的彩色图像边缘检测算法。将聚类嵌入到边缘检测中，使这2类图像分割方法有效结合，增强了2类方法的优势。实验结果表明，该算法有效解决了传统蚁群算法（ACO）收敛较慢的问题，较好地保留了图像边缘细节，降低了计算复杂度，与典型分割方法相比具有更好的性能。

关键词: image segmentation, edge detection, K-means, clustering, ant colony optimization

Abstract: In order to improve the accuracy of image edge detection, this paper proposes a color image edge detection algorithm based on K-means ant colony optimization. By embedding the clustering in edge detection, the two kinds of algorithms about image segmentation are combined effectively and the advantages are enhanced. Experimental results show that the proposed algorithm solves the problem of slow convergence in traditional ACO. Compared with the typical segmentation methods, it also better retains the image edge details and reduces the computational complexity, which has a better performance.

Key words: image segmentation, edge detection, K-means, clustering, ant colony optimization

中图分类号:

TP391

刘波平1,2,孟莎莎1,2,饶兰香1,2,付康1,2. 基于K-均值改进蚁群优化的彩色图像边缘检测算法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2016.09.013.

LIU Bo-ping1,2, MENG Sha-sha1,2, RAO Lan-xiang1,2, FU Kang1,2. Color Image Edge Detection Algorithm Based on K-means and Improved Ant Colony Optimization[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.09.013.

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基于K-均值改进蚁群优化的彩色图像边缘检测算法

Color Image Edge Detection Algorithm Based on K-means and Improved Ant Colony Optimization

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