Name
Abstract | ||
|---|---|---|
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. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288038 | ICASSP |
Keywords | Field | DocType |
compact descriptor,mobile visual search,universal quantization,visual vocabulary,encoding,quantization,image retrieval,visualization,mobile computing,learning artificial intelligence,mobile communication | Offline learning,Pattern recognition,Computer science,Neural coding,Image retrieval,Coding (social sciences),Artificial intelligence,Vocabulary,Discriminative model,Codebook,Encoding (memory) | Conference |
Volume | Issue | Citations |
null | null | 6 |
PageRank | References | Authors |
0.45 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Lin | 1 | 3495 | 502.80 |
| Ling-yu Duan | 2 | 1770 | 124.87 |
| Jie Chen | 3 | 2487 | 353.65 |
| Rongrong Ji | 4 | 3616 | 189.98 |
| Siwei Luo | 5 | 244 | 41.85 |
| Wen Gao | 6 | 11374 | 741.77 |