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Conference Paper: Learning regularized, query-dependent bilinear similarities for large scale image retrieval

TitleLearning regularized, query-dependent bilinear similarities for large scale image retrieval
Authors
Issue Date2013
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001809
Citation
The 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, OR., 23-28 June 2013. In Conference Proceedings, 2013, p. 413-420 How to Cite?
AbstractAn effective way to improve the quality of image retrieval is by employing a query-dependent similarity measure. However, implementing this in a large scale system is non-trivial because we want neither hurting the efficiency nor relying on too many training samples. In this paper, we introduce a query-dependent bilinear similarity measure to address the first issue. Based on our bilinear similarity model, query adaptation can be achieved by simply applying any existing efficient indexing/retrieval method to a transformed version (surrogate) of a query. To address the issue of limited training samples, we further propose a novel angular regularization constraint for learning the similarity measure. The learning is formulated as a Quadratic Programming (QP) problem and can be solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-million web-image dataset validate that our proposed method can consistently bring improvements and the whole solution is practical in large scale applications.
Persistent Identifierhttp://hdl.handle.net/10722/189617
ISBN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKuang, Zen_US
dc.contributor.authorSun, Jen_US
dc.contributor.authorWong, KKYen_US
dc.date.accessioned2013-09-17T14:50:21Z-
dc.date.available2013-09-17T14:50:21Z-
dc.date.issued2013en_US
dc.identifier.citationThe 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, OR., 23-28 June 2013. In Conference Proceedings, 2013, p. 413-420en_US
dc.identifier.isbn978-0-7695-4990-3-
dc.identifier.urihttp://hdl.handle.net/10722/189617-
dc.description.abstractAn effective way to improve the quality of image retrieval is by employing a query-dependent similarity measure. However, implementing this in a large scale system is non-trivial because we want neither hurting the efficiency nor relying on too many training samples. In this paper, we introduce a query-dependent bilinear similarity measure to address the first issue. Based on our bilinear similarity model, query adaptation can be achieved by simply applying any existing efficient indexing/retrieval method to a transformed version (surrogate) of a query. To address the issue of limited training samples, we further propose a novel angular regularization constraint for learning the similarity measure. The learning is formulated as a Quadratic Programming (QP) problem and can be solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-million web-image dataset validate that our proposed method can consistently bring improvements and the whole solution is practical in large scale applications.-
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001809-
dc.relation.ispartofIEEE Conference on Computer Vision and Pattern Recognition Workshops Proceedingsen_US
dc.rightsIEEE Conference on Computer Vision and Pattern Recognition Workshops Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleLearning regularized, query-dependent bilinear similarities for large scale image retrievalen_US
dc.typeConference_Paperen_US
dc.identifier.emailWong, KKY: kykwong@cs.hku.hken_US
dc.identifier.authorityWong, KKY=rp01393en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CVPRW.2013.69-
dc.identifier.hkuros221062en_US
dc.identifier.spage413-
dc.identifier.epage420-
dc.identifier.isiWOS:000331116100066-
dc.publisher.placeUnited Statesen_US
dc.customcontrol.immutablesml 131007-

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