Accurate Image Retrieval with Unsupervised 2-Stage k-NN Re-Ranking

Abstract

Many state-of-the-art image retrieval systems include a re-ranking step to refine the suggested initial ranking list so as to improve the retrieval accuracy. In this paper, we present a novel 2-stage k-NN re-ranking algorithm. In stage one, we generate an expanded list of candidate database images for re-ranking so that lower ranked ground truth images will be included and re-ranked. In stage two, we re-rank the list of candidate images using a confidence score which is calculated based on, rRBO, a new proposed ranking list similarity measure. In addition, we propose the rLoCATe image feature, which captures robust color and texture information on salient image patches, and shows superior performance in the image retrieval task. We evaluate the proposed re-ranking algorithm on various initial ranking lists created using both SIFT and rLoCATe on two popular benchmark datasets along with a large-scale one million distraction dataset. The results show that our proposed algorithm is not sensitive for different parameter configurations and it outperforms existing k-NN re-ranking methods.