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.