QsRank: Query-sensitive hash code ranking for efficient -neighbor search

Although binary hash code-based image indexing methods have been recently developed for large-scale applications, the problem of ranking such hash codes has been barely studied. In this paper, we propose a query sensitive ranking algorithm (QsRank) to rank PCA-based hash codes for the ∊-neighbor search problem. The QsRank algorithm takes the target neighborhood radius ∊ and the raw feature of a given query as input, and models the statistical properties of the target ∊-neighbors in the space of hash codes. Unlike the Hamming distance, the proposed algorithm does not compress query points to hash codes. Therefore, it suffers less information loss and is more effective than Hamming distance-based approaches. Based on the QsRank method, we developed an efficient indexing structure and retrieval algorithm for large-scale ∊-neighbor search. Evaluations on two datasets of 10 million web images and 10 million SIFT descriptors demonstrate that the proposed retrieval system achieves higher accuracy with less memory cost and faster speed.