Active Reranking for Web Image Search

Image search Reranking methods usually fail to capture the user's intention when the query term is ambiguous.Therefore, Reranking with user interactions, or active Reranking, is highly demanded to effectively improve the search performance. The essential problem in active Reranking is how to target the user's intention. To complete this goal, this paper presents a structural information based sample selection strategy to reduce the user's labeling efforts. Furthermore, to localize the user's intention in the visual feature space, a novel local-global discriminative dimension reduction algorithm is proposed. In this algorithm, a sub manifold is learned by transferring the local geometry and the discriminative information from the labeled images to the whole (global) image database. Experiments on both synthetic datasets and a real Web image search dataset demonstrate the effectiveness of the proposed active Reranking scheme, including both the structural information based active sample selection strategy and the local-global discriminative dimension reduction algorithm. Index Terms-Active reranking, local-global discriminative (LGD) dimension reduction, structural information (SInfo) based active sample selection, web image search reranking.