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- Publisher Website: 10.1109/TCSVT.2013.2276713
- Scopus: eid_2-s2.0-84897985341
- WOS: WOS:000334522800013
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Article: Semisupervised hashing via kernel hyperplane learning for scalable image search
Title | Semisupervised hashing via kernel hyperplane learning for scalable image search |
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Authors | |
Keywords | Kernel hyperplane learning Multiple kernel learning (MKL) Semisupervised hashing |
Issue Date | 2014 |
Citation | IEEE Transactions on Circuits and Systems for Video Technology, 2014, v. 24, n. 4, p. 704-713 How to Cite? |
Abstract | Hashing methods that aim to seek a compact binary code for each image are demonstrated to be efficient for scalable content-based image retrieval. In this paper, we propose a new hashing method called semisupervised kernel hyperplane learning (SKHL) for semantic image retrieval by modeling each hashing function as a nonlinear kernel hyperplane constructed from an unlabeled dataset. Moreover, a Fisher-like criterion is proposed to learn the optimal kernel hyperplanes and hashing functions, using only weakly labeled training samples with side information. To further integrate different types of features, we also incorporate multiple kernel learning (MKL) into the proposed SKHL (called SKHL-MKL), leading to better hashing functions. Comprehensive experiments on CIFAR-100 and NUS-WIDE datasets demonstrate the effectiveness of our SKHL and SKHL-MKL. © 2013 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/321208 |
ISSN | 2023 Impact Factor: 8.3 2023 SCImago Journal Rankings: 2.299 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kan, Meina | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Shan, Shiguang | - |
dc.contributor.author | Chen, Xilin | - |
dc.date.accessioned | 2022-11-03T02:17:22Z | - |
dc.date.available | 2022-11-03T02:17:22Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | IEEE Transactions on Circuits and Systems for Video Technology, 2014, v. 24, n. 4, p. 704-713 | - |
dc.identifier.issn | 1051-8215 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321208 | - |
dc.description.abstract | Hashing methods that aim to seek a compact binary code for each image are demonstrated to be efficient for scalable content-based image retrieval. In this paper, we propose a new hashing method called semisupervised kernel hyperplane learning (SKHL) for semantic image retrieval by modeling each hashing function as a nonlinear kernel hyperplane constructed from an unlabeled dataset. Moreover, a Fisher-like criterion is proposed to learn the optimal kernel hyperplanes and hashing functions, using only weakly labeled training samples with side information. To further integrate different types of features, we also incorporate multiple kernel learning (MKL) into the proposed SKHL (called SKHL-MKL), leading to better hashing functions. Comprehensive experiments on CIFAR-100 and NUS-WIDE datasets demonstrate the effectiveness of our SKHL and SKHL-MKL. © 2013 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Circuits and Systems for Video Technology | - |
dc.subject | Kernel hyperplane learning | - |
dc.subject | Multiple kernel learning (MKL) | - |
dc.subject | Semisupervised hashing | - |
dc.title | Semisupervised hashing via kernel hyperplane learning for scalable image search | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCSVT.2013.2276713 | - |
dc.identifier.scopus | eid_2-s2.0-84897985341 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 704 | - |
dc.identifier.epage | 713 | - |
dc.identifier.isi | WOS:000334522800013 | - |