Particle Swarm Image Segmentation Based on K-means

被引：0
|
作者：
Xu, Yongfeng [1 ]
Zhang, Bo [1 ]
Su, Yongli [1 ]
机构：
[1] Northwest Univ, Dept Math, Xian 710069, Shaanxi, Peoples R China
来源：
2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL IV | 2010年
关键词：
image segmentation; particle swarm optimization; k-means;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A new algorithm for image segmentation based on k-mean and particle swarm optimization algorithm is proposed in the paper. K-mean clustering algorithm is a local search algorithm because it is easily trapped local optimum and is sensitive to initial value effectively. On the other hand, particle swarm optimization algorithm is a global optimization algorithm. By incorporating the local search ability of k-mean algorithm and the global optimization ability of PSO and taking the criterion function of k-mean as the object function of PSO, a new hybrid color image segmentation algorithm based on particle swarm optimization and k-mean algorithm is proposed. Experiments show that the new algorithm can get the optimal quantizated image by PSNR and RMSE.
引用
收藏
页码：488 / 491
页数：4
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