基于改进k-means算法的数字图像聚类

Digital image clustering based on improved k-means algorithm

针对海量彩色图像聚类问题,本文引入改进型k-means算法并将其应用于彩色图像聚类中。该算法由类内-类间距离加权k-means算法和基于近邻传播聚类算法的类数量上界确定方法组成。在实验中,彩色图像的亮度分量的局部二值模式(Local Binary Pattern,LBP)图被重组成行向量,然后构成样本集,本文所提出的改进型k-means算法被用于对样本集进行聚类处理。实验结果显示,在多个聚类方法常用的评价指标上,本方法相比于传统方法达到了更高的聚类准确度。同时,相比于传统方法,本方法也更具有执行效率。

For the mass image clustering problem,the improved k-means algorithm is proposed and applied to the color image clustering.The algorithm consists of intraclass-interclass distance weighted k-means algorithm and nearest neighbor propagation clustering algorithm.In the experiment,the LBP map of the luminance component of the color image is reconstructed into a row vector and then constitutes a sample set.The improved k-means algorithm proposed in this paper is used to cluster the sample set.The experimental results show that the proposed method achieves higher clustering accuracy than the traditional methods in the evaluation indicators commonly used in multiple clustering methods.At the same time,the method is more efficient than traditional methods.