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dc.contributor.authorYang, Haichuan
dc.contributor.authorBai, Xiao
dc.contributor.authorZhou, Jun
dc.contributor.authorRen, Peng
dc.contributor.authorCheng, Jian
dc.contributor.authorBai, Lu
dc.contributor.editorLongbing Cao, George Karypis, Irwin King, Wei Wang
dc.date.accessioned2017-05-03T16:12:01Z
dc.date.available2017-05-03T16:12:01Z
dc.date.issued2014
dc.identifier.isbn9781479969913
dc.identifier.refurihttp://datamining.it.uts.edu.au/conferences/dsaa14/
dc.identifier.urihttp://hdl.handle.net/10072/67949
dc.description.abstractIn information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic research problem in hashing. Most hashing methods used purely randomized or optimized learning strategy to achieve this goal. Our method, on the other hand, combines both randomized and optimized strategies. It starts from generating multiple random vectors, and then approximates them by a single projection vector. In the quantization step, it uses the orthogonal transformation to minimize an upper bound of the deviation between real-valued vectors and binary codes. The proposed method overcomes the problem that randomized hash functions are isolated from the data distribution. What's more, our method supports an arbitrary number of hash functions, which is beneficial in building better hashing methods. The experiments show that our approach outperforms the alternative state-of-the-art methods for retrieval on the large scale dataset.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent265433 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttps://ieeexplore.ieee.org/xpl/conhome/7050498/proceeding
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameIEEE International Conference on Data Science & Advanced Analytics
dc.relation.ispartofconferencetitle2014 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA)
dc.relation.ispartofdatefrom2014-10-30
dc.relation.ispartofdateto2014-11-01
dc.relation.ispartoflocationDept Comp Sci & Technol Shanghai Jiaotong Univ, Shanghai, PEOPLES R CHINA
dc.relation.ispartofpagefrom53
dc.relation.ispartofpageto58
dc.rights.retentionY
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchcode460304
dc.titleSemi-randomized Hashing for Large Scale Data Retrieval
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2015-05-21T02:40:09Z
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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