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dc.contributor.authorCakir, Fatihen_US
dc.contributor.authorBargal, Sarah Adelen_US
dc.contributor.authorSclaroff, Stanen_US
dc.date.accessioned2018-02-05T18:50:37Z
dc.date.available2018-02-05T18:50:37Z
dc.date.issued2017-03
dc.identifier.citationFatih Cakir, Sarah Adel Bargal, Stan Sclaroff. 2017. "Online supervised hashing." Computer Vision and Image Understanding, Volume 156, pp. 162 - 173.
dc.identifier.issn1077-3142
dc.identifier.urihttps://hdl.handle.net/2144/26691
dc.description.abstractFast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over unsupervised methods. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies may be inefficient when confronted with large datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as the dataset continues to grow and new variations appear over time. To handle these issues, we propose OSH: an Online Supervised Hashing technique that is based on Error Correcting Output Codes. We consider a stochastic setting where the data arrives sequentially and our method learns and adapts its hashing functions in a discriminative manner. Our method makes no assumption about the number of possible class labels, and accommodates new classes as they are presented in the incoming data stream. In experiments with three image retrieval benchmarks, our method yields state-of-the-art retrieval performance as measured in Mean Average Precision, while also being orders-of-magnitude faster than competing batch methods for supervised hashing. Also, our method significantly outperforms recently introduced online hashing solutions.en_US
dc.description.urihttps://pdfs.semanticscholar.org/555b/de4f14630d8606e37096235da8933df228f1.pdf
dc.format.extent162 - 173en_US
dc.relation.ispartofComputer Vision and Image Understanding
dc.subjectArtificial intelligence and image processingen_US
dc.subjectCognitive scienceen_US
dc.subjectArtificial intelligence & image processingen_US
dc.subjectFast similarity searchen_US
dc.subjectHashingen_US
dc.subjectApproximate nearest neighborsen_US
dc.titleOnline supervised hashingen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1016/j.cviu.2016.10.009
pubs.elements-sourcecrossrefen_US
pubs.notespublisher: Elsevier articletitle: Online supervised hashing journaltitle: Computer Vision and Image Understanding articlelink: http://dx.doi.org/10.1016/j.cviu.2016.10.009 content_type: article copyright: © 2016 Elsevier Inc. All rights reserved.en_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-0711-4313 (Sclaroff, Stan)


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