Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84278
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dc.contributor.authorLin, G.en
dc.contributor.authorShen, C.en
dc.contributor.authorShi, Q.en
dc.contributor.authorVan Den Hengel, A.en
dc.contributor.authorSuter, D.en
dc.date.issued2014en
dc.identifier.citationProceedings, 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, 24-27 June 2014, Columbus, Ohio, USA / pp.1963-1970en
dc.identifier.isbn9781479951178en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/2440/84278-
dc.description.abstractSupervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval performance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash functions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for highdimensional data, our method is orders of magnitude faster than many methods in terms of training time.en
dc.description.statementofresponsibilityGuosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suteren
dc.description.urihttp://www.pamitc.org/cvpr14/en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rights© 2014 IEEEen
dc.source.urihttp://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Lin_Fast_Supervised_Hashing_2014_CVPR_paper.htmlen
dc.titleFast supervised hashing with decision trees for high-dimensional dataen
dc.typeConference paperen
dc.identifier.rmid0030000002en
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (2014 : Columbus, Ohio)en
dc.identifier.doi10.1109/CVPR.2014.253en
dc.identifier.pubid64008-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS01en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]en
Appears in Collections:Computer Science publications

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