改进的PCM聚类算法在图像分割中的应用  

Improved PCM Clustering Algorithm and Its Application in Image Segmentation

作　　者：左浩[1] 李雯[1] 

机构地区：[1]江西理工大学信息工程学院,赣州341000

出　　处：《计算机与数字工程》2010年第11期148-151,共4页Computer & Digital Engineering

摘　　要：可能性C均值聚类算法(PCM)对于噪声显示了良好的鲁棒性,但是它没有考虑到像素的空间信息,在含有大量噪声的情况下,PCM算法的分割性能会大大降低。基于PCM算法,提出了一种改进的PCM算法,该算法改进了隶属度函数,新的像素点隶属度更新为其邻域隶属度的几何均值。实验结果显示新的算法能够更有效的分割图像,并显示出良好的抗噪能力。Possibilistic c-means clustering algorithm（PCM） exhibits the robustness to noises,but the pixel spatial information is not considered in this algorithm,in the case of a large number of noises,PCM algorithm will be degraded.Based on PCM algorithm,an improved algorithm is proposed for image segmentation by improving membership function,the new membership of the pixel is updated to the geometric mean value of its neighborhood membership.The experimental results show that the new algorithm can segment the image effectively and properly,and has good performance of resisting noises.

关 键 词：模糊C均值 可能性C均值 图像分割 聚类 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]