Boost image clustering with user query log

Image clustering is to derive a salient grouping of images such that similar ones are placed in the same cluster, which is useful in many applications. In this paper, we propose a constrained clustering algorithm, which leverages the collected user query log to guide the clustering process. Our method models a set of images as a graph and randomly contracts two vertices into a meta vertex iteratively with regarding to their similarity until the desired number of image groups has been reached. The experimental results demonstrate the superiority of our proposal.