期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 36, 期 4, 页码 824-830出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2013.225
关键词
Similarity-sensitive hashing; metric learning; feature descriptor; neural network
资金
- ERC [335491, 307047]
- European Research Council (ERC) [335491] Funding Source: European Research Council (ERC)
We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra-and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.
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