现代雷达2016，Vol.38Issue(11)：30-34,39,6.DOI:10.16592/j.cnki.1004-7859.2016.11.007
基于蚁群算法的模糊C均值聚类的改进研究
Improved Fuzzy C-means Clustering Based on Ant Colony Algorithm
高晋凯 1侯文 1杨冰倩 1王贇贇1
作者信息
- 1. 中北大学信息与通信工程学院，太原030051
- 折叠
摘要
Abstract
In the study of image segmentation , fuzzy C-means clustering algorithm ( FCM) has been greatly improved compared to the previous hard clustering , which is a clustering algorithm based on a function of best practices .However, the clustering center and number are difficult to be determined for the traditional FCM , also the search process is easy to fall into local optimum .So an improved FCM clustering algorithm is proposed based on the ant colony algorithm .The improved algorithm uses the global optimiza-tion features and strong characteristics of robustness of ant colony algorithm .The cluster centers and number obtained by ant colony algorithm are applied to a traditional FCM algorithm to make up for the shortcomings of the traditional FCM .The improved algo-rithm improves the image segmentation accuracy by processing the image blocks and introducing the multi -scale gradient .Finally the effectiveness and the practicality of the improved algorithm is verified through the experiments .关键词
图像分割/蚁群算法/模糊C均值聚类/梯度Key words
image segmentation/ant colony algorithm/fuzzy C-means clustering/gradient分类
信息技术与安全科学引用本文复制引用
高晋凯,侯文,杨冰倩,王贇贇..基于蚁群算法的模糊C均值聚类的改进研究[J].现代雷达,2016,38(11):30-34,39,6.
