Spectral Images and Features Co-Clustering with Application to Content-based Image Retrieval

In this paper, we present a spectral graph partitioning method for the co-clustering of images and features. We present experimental results, which show that spectral co-clustering has computational advantages over traditional k-means algorithm, especially when the dimensionalities of feature vectors are high. In the context of image clustering, we also show that spectral co-clustering gives better performances. We advocate that the images and features co-clustering framework offers new opportunities for developing advanced image database management technology and illustrate a possible scheme for exploiting the co-clustering results for developing a novel content-based image retrieval method