期刊
IEEE SIGNAL PROCESSING LETTERS
卷 23, 期 6, 页码 893-897出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2016.2517093
关键词
Cross-view; hashing; multimedia retrieval
资金
- National Science Foundation of China [61273251, 61402203]
- Doctoral Candidate Innovation Project Fund of Jiangsu province [KYLX_0385]
Hashing techniques have been widely applied to large-scale cross-view retrieval tasks due to the significant advantage of binary codes in computation and storage efficiency. However, most existing cross-view hashing methods learn binary codes with continuous relaxations, which cause large quantization loss across views. To address this problem, in this letter, we propose a novel cross-view hashing method, where a common Hamming space is learned such that binary codes from different views are consistent and comparable. The quantization loss across views is explicitly reduced by two carefully designed regression terms from original spaces to the Hamming space. In our method, the l(2,1)-norm regularization is further exploited for discriminative feature selection. To obtain high-quality binary codes, we propose to jointly learn the codes and hash functions, for which an efficient iterative algorithm is presented. We evaluate the proposed method, dubbed Robust Cross-view Hashing (RCH), on two benchmark datasets and the results demonstrate the superiority of RCH over many other state-of-the-art methods in terms of retrieval performance and cross-view consistency.
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