基于遗传模糊核聚类的图像分割

Images Segmentation Based on Genetic_Kernel Fuzzy C-Means Clustering Algorithm

针对红外图像的特点和模糊聚类算法对噪声、初始聚类中心敏感等问题,提出一种遗传模糊核聚类算法.该算法对红外图像像素灰度值进行全局的聚类分析并计算最优的聚类中心和隶属度矩阵,根据聚类结果和最大隶属度原则进行红外图像分割.通过实验验证,文中算法能较好地分割含高斯噪声、背景简单或复杂的红外图像.

Aiming at the characteristics of infrared images and the sensitivity of fuzzy clustering algorithm to the noise and the initial clustering center, a genetic kernel fuzzy C-Means clustering algorithm（G_KFCM） is presented. The gray values of the infrared images are clustered globally. Then the optimal clustering center and the membership matrix are calculated by the G_KFCM. The image segmentation is performed according to the clustering result and the maximum membership principle. The experimental results show G_KFCM is effective to the infrared images respectirely including Gaussian noise, simple or complex background.