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AuthorOzan, Ezgi Can
AuthorKiranyaz, Serkan
AuthorGabbouj, Moncef
Available date2021-09-05T05:40:11Z
Publication Date2016
Publication NameIEEE Transactions on Knowledge and Data Engineering
ResourceScopus
ISSN10414347
URIhttp://dx.doi.org/10.1109/TKDE.2016.2597834
URIhttp://hdl.handle.net/10576/22672
AbstractIn this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.
Languageen
PublisherIEEE Computer Society
SubjectApproximate nearest neighbor search
binary codes
large-scale retrieval
vector quantization
TitleCompetitive Quantization for Approximate Nearest Neighbor Search
TypeArticle
Pagination2884-2894
Issue Number11
Volume Number28
dc.accessType Open Access


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