Abstract: Unsupervised clustering of images aims to partition the whole image set into several subsets on the basis of image data itself,while without any priori information.As dimensionality of an image is usually very high,curse of dimensionality arises du-ring the image processing.Having analyzed the problem of images clustering,a novel unsupervised image clustering algorithm is proposed.The proposed algorithm is based on diffused interface model on graph.Images were encoded as the data points in high dimensional observing space,and then were projected into low dimensional feature space.Diffuse interface model based unsupervised clustering algorithm was constructed in feature space,and dimension reduction operator was introduced into the model.Loop iterative algorithm was employed to optimize the energy function of diffuse interface model.The optimized diffuse interface was adopted to cluster images into different subsets.Experimental results show that the proposed algorithm is superior to traditional K-means,DBSCAN and Spectral Clustering algorithm.It achieves better clustering results and lower error rates.

Key words: Diffuse interface, Dimension reduction, Image clustering, Optimization, Unsupervised learning