File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Semisupervised hashing via kernel hyperplane learning for scalable image search

TitleSemisupervised hashing via kernel hyperplane learning for scalable image search
Authors
KeywordsKernel hyperplane learning
Multiple kernel learning (MKL)
Semisupervised hashing
Issue Date2014
Citation
IEEE Transactions on Circuits and Systems for Video Technology, 2014, v. 24, n. 4, p. 704-713 How to Cite?
AbstractHashing 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 Identifierhttp://hdl.handle.net/10722/321208
ISSN
2023 Impact Factor: 8.3
2023 SCImago Journal Rankings: 2.299
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKan, Meina-
dc.contributor.authorXu, Dong-
dc.contributor.authorShan, Shiguang-
dc.contributor.authorChen, Xilin-
dc.date.accessioned2022-11-03T02:17:22Z-
dc.date.available2022-11-03T02:17:22Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Circuits and Systems for Video Technology, 2014, v. 24, n. 4, p. 704-713-
dc.identifier.issn1051-8215-
dc.identifier.urihttp://hdl.handle.net/10722/321208-
dc.description.abstractHashing 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.languageeng-
dc.relation.ispartofIEEE Transactions on Circuits and Systems for Video Technology-
dc.subjectKernel hyperplane learning-
dc.subjectMultiple kernel learning (MKL)-
dc.subjectSemisupervised hashing-
dc.titleSemisupervised hashing via kernel hyperplane learning for scalable image search-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TCSVT.2013.2276713-
dc.identifier.scopuseid_2-s2.0-84897985341-
dc.identifier.volume24-
dc.identifier.issue4-
dc.identifier.spage704-
dc.identifier.epage713-
dc.identifier.isiWOS:000334522800013-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats