Image segmentation based on ant colony optimization and K-means clustering

被引：8
|
作者：
Zhao, Bo [1 ]
Zhu, Zhongxiang [1 ]
Mao, Enrong [1 ]
Song, Zhenghe [1 ]
机构：
[1] China Agr Univ, Coll Engn, Beijing, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6 | 2007年
关键词：
ant colony optimization; K-means clustering; image segmentation;
D O I：
10.1109/ICAL.2007.4338607
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
According to the characteristics of the ant colony optimization and the K-means clustering, a method for the image segmentation based on the ant colony optimization and the K-means clustering was proposed in this paper. Firstly, the basic principle of the two algorithms were introduced. Secondly, their characteristics on the image segmentation were analyzed. Finally the improved algorithm was proposed, this algorithm can effectively overcome shortages which are the slow rate of the ant colony optimization and the K-means clustering dependent on the initial clustering centers. Experimental results proved that the improved algorithm was an effective method for the image segmentation in the practical application, which could segment the object accurately.
引用
收藏
页码：459 / 463
页数：5
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