改进FCM的图像聚类方法

Image Clustering Based on Improved FCM Algorithm

对传统FCM算法的隶属度函数进行了改进,改进后的算法有效降低了孤立点对图像数据聚类结果的影响。通过灰度-梯度共生矩阵对图像进行纹理特征提取,利用主分量分析法对提取后的图像高维特征进行降维处理,结合本文改进的FCM图像聚类算法对预处理后的图像数据进行聚类。实验证明,该方法具有较好的聚类效果,且能以较少的迭代次数达到全局最优。

In this paper,the traditional FCM algorithm membership function was improved.The improved algorithm can reduce the isolation point of the image data clustering results.In this paper,Gray-gradient co-occurrence matrix of the image texture feature extraction using principal component analysis on the extracted high-dimensional feature image to reduce the dimensions,combined with this improved FCM clustering algorithm to the image after the image data preprocessing clustering.Experiments show that the method has better clustering results,with fewer iterations and can reach the global optimum.