Diverse fuzzy c-means for image clustering

Abstract

Image clustering is a key technique for better accomplishing image annotation and searching in large image repositories. Fuzzy c-means and its variations have achieved excellent performance on image clustering because they allow each image to belong to more than one cluster. However, these methods neglect the relations between different image clusters, and hence often suffer from the “cluster one-sidedness” problem that redundant centers are learned to characterize the same or similar image clusters. To this issue, we propose a diverse fuzzy c-means for image clustering via introducing a novel diversity regularization into the traditional fuzzy c-means objective. This diversity regularization guarantees the learned image cluster centers to be different from each other and to fill the image data space as much as possible. An efficient optimization algorithm is exploited to address the diverse fuzzy c-means objective, which is proved to converge to local optimal solutions and has a satisfactory time complexity. Experiments on synthetic and six image datasets demonstrate the effectiveness of the proposed method as well as the necessity of the diversity regularization.