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Conference Paper: Sparse similarity matrix learning for visual object retrieval

TitleSparse similarity matrix learning for visual object retrieval
Authors
KeywordsBenchmark datasets
Discriminability
Object retrieval
Off-diagonal elements
Quantization errors
Similarity matrix
Similarity metrics
TF-IDF weighting
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500
Citation
The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX., 4-9 August 2013. In Conference Proceedings, 2013, p. 1-8 How to Cite?
AbstractTf-idf weighting scheme is adopted by state-of-the-art object retrieval systems to reflect the difference in discriminability between visual words. However, we argue it is only suboptimal by noting that tf-idf weighting scheme does not take quantization error into account and exploit word correlation. We view tf-idf weights as an example of diagonal Mahalanobis-type similarity matrix and generalize it into a sparse one by selectively activating off-diagonal elements. Our goal is to separate similarity of relevant images from that of irrelevant ones by a safe margin. We satisfy such similarity constraints by learning an optimal similarity metric from labeled data. An effective scheme is developed to collect training data with an emphasis on cases where the tf-idf weights violates the relative relevance constraints. Experimental results on benchmark datasets indicate the learnt similarity metric consistently and significantly outperforms the tf-idf weighting scheme. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/186495
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYan, Zen_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2013-08-20T12:11:14Z-
dc.date.available2013-08-20T12:11:14Z-
dc.date.issued2013en_US
dc.identifier.citationThe 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX., 4-9 August 2013. In Conference Proceedings, 2013, p. 1-8en_US
dc.identifier.isbn978-1-4673-6128-6-
dc.identifier.urihttp://hdl.handle.net/10722/186495-
dc.description.abstractTf-idf weighting scheme is adopted by state-of-the-art object retrieval systems to reflect the difference in discriminability between visual words. However, we argue it is only suboptimal by noting that tf-idf weighting scheme does not take quantization error into account and exploit word correlation. We view tf-idf weights as an example of diagonal Mahalanobis-type similarity matrix and generalize it into a sparse one by selectively activating off-diagonal elements. Our goal is to separate similarity of relevant images from that of irrelevant ones by a safe margin. We satisfy such similarity constraints by learning an optimal similarity metric from labeled data. An effective scheme is developed to collect training data with an emphasis on cases where the tf-idf weights violates the relative relevance constraints. Experimental results on benchmark datasets indicate the learnt similarity metric consistently and significantly outperforms the tf-idf weighting scheme. © 2013 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500-
dc.relation.ispartofInternational Joint Conference on Neural Networks (IJCNN)en_US
dc.rightsInternational Joint Conference on Neural Networks (IJCNN). Copyright © IEEE.-
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.subjectBenchmark datasets-
dc.subjectDiscriminability-
dc.subjectObject retrieval-
dc.subjectOff-diagonal elements-
dc.subjectQuantization errors-
dc.subjectSimilarity matrix-
dc.subjectSimilarity metrics-
dc.subjectTF-IDF weighting-
dc.titleSparse similarity matrix learning for visual object retrievalen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y: yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IJCNN.2013.6707063-
dc.identifier.scopuseid_2-s2.0-84893554076-
dc.identifier.hkuros220947en_US
dc.identifier.spage1-
dc.identifier.epage8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 150204-

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