Multimodal Similarity-Preserving Hashing
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- a...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2014-04, Vol.36 (4), p.824-830 |
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| container_end_page | 830 |
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| container_issue | 4 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 36 |
| creator | Masci, Jonathan Bronstein, Michael M. Bronstein, Alexander M. Schmidhuber, Jürgen |
| description | 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. |
| doi_str_mv | 10.1109/TPAMI.2013.225 |
| format | Article |
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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.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2013.225</identifier><identifier>PMID: 26353203</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Applied sciences ; Architecture (computers) ; Artificial intelligence ; Computer science; control theory; systems ; Connectionism. Neural networks ; Data processing. List processing. 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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.</description><subject>Applied sciences</subject><subject>Architecture (computers)</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Data processing. List processing. Character string processing</subject><subject>Exact sciences and technology</subject><subject>feature descriptor</subject><subject>Information systems. 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Data processing</subject><subject>metric learning</subject><subject>Multimedia</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Nonlinear dynamics and nonlinear dynamical systems</subject><subject>Optimization</subject><subject>Physics</subject><subject>Representations</subject><subject>Similarity</subject><subject>Similarity-sensitive hashing</subject><subject>Software</subject><subject>Statistical physics, thermodynamics, and nonlinear dynamical systems</subject><subject>Synchronization ; coupled oscillators</subject><subject>Tasks</subject><subject>Training</subject><subject>Vectors</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0EtLAzEQwPEgitbq1YsgBRG8bJ3Jq8mxFB8Fi4J6XrIxqyn7qElX6Lc32qrgxdNA8stA_oQcIQwRQV883o9n0yEFZENKxRbpoWY6Y4LpbdIDlDRTiqo9sh_jHAC5ALZL9qhMggLrkfNZVy193T6bavDga1-Z4Jer7D646MK7b14GNya-pnlAdkpTRXe4mX3ydHX5OLnJbu-up5PxbWY5yGWGheMalQNpjbGiEEIDLZSSNB0oLEpkvJQjwUEzW2hOeVFQjoIrsHrkgPXJ-XrvIrRvnYvLvPbRuqoyjWu7mOMIUTAhQf1PBQOtqdY60dM_dN52oUkfSSqFHNGUKqnhWtnQxhhcmS-Cr01Y5Qj5Z-78K3f-mTtPudODk83arqjd8w__7pvA2QaYaE1VBtNYH3-d4sAkyuSO1847536upRQcOWcfK9uMrA</recordid><startdate>20140401</startdate><enddate>20140401</enddate><creator>Masci, Jonathan</creator><creator>Bronstein, Michael M.</creator><creator>Bronstein, Alexander M.</creator><creator>Schmidhuber, Jürgen</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2014-04, Vol.36 (4), p.824-830 |
| issn | 0162-8828 1939-3539 2160-9292 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Architecture (computers) Artificial intelligence Computer science control theory systems Connectionism. Neural networks Data processing. List processing. Character string processing Exact sciences and technology feature descriptor Information systems. Data bases Learning Mathematical analysis Measurement Memory organisation. Data processing metric learning Multimedia neural network Neural networks Nonlinear dynamics and nonlinear dynamical systems Optimization Physics Representations Similarity Similarity-sensitive hashing Software Statistical physics, thermodynamics, and nonlinear dynamical systems Synchronization coupled oscillators Tasks Training Vectors |
| title | Multimodal Similarity-Preserving Hashing |
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