Learning multiple codebooks for low bit rate mobile visual search
Compressing a query image's signature via vocabulary coding is an effective approach to low bit rate mobile visual search. State-of-the-art methods concentrate on offline learning a codebook from an initial large vocabulary. Over a large heterogeneous reference database, learning a single codebook may not suffice for maximally removing redundant codewords for vocabulary based compact descriptor. In this paper, we propose to learn multiple codebooks (m-Codebooks) for extremely compressing image signatures. A query-specific codebook (q-Codebook) is online generated at both client and server sides by adaptively weighting the off-line learned multiple codebooks. The q-Codebook is subsequently employed to quantize the query image for producing compact, discriminative, and scalable descriptors. As q-Codebook may be simultaneously generated at both sides, without transmitting the entire vocabulary, only small overhead (e.g. codebook ID and codeword 0/1 index) is incurred to reconstruct the query signature at the server end. To fulfill m-Codebooks and q-Codebook, we adopt a Bi-layer Sparse Coding method to learn the sparse relationships of codewords vs. codebooks as well as codebooks vs. query images via l1 regularization. Experiments on benchmarking datasets have demonstrated the extremely small descriptor's supervior performance in image retrieval.