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Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance

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dc.contributor.author Ocsa, Alexander es_PE
dc.contributor.author Huillca, Jose Luis es_PE
dc.contributor.author Coronado, Ricardo es_PE
dc.contributor.author Quispe, Oscar es_PE
dc.contributor.author Arbieto, Carlos es_PE
dc.contributor.author Lopez, Cristian es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2017-11
dc.description.abstract The growing volume of data and its increasing complexity require even more efficient and faster information retrieval techniques. Approximate nearest neighbor search algorithms based on hashing were proposed to query high-dimensional datasets due to its high retrieval speed and low storage cost. Recent studies promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1109/la-cci.2017.8285730
dc.identifier.uri https://hdl.handle.net/20.500.12390/1273
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
dc.rights info:eu-repo/semantics/openAccess
dc.subject feedforward neural nets
dc.subject convolution es_PE
dc.subject data structures es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#5.08.02
dc.title Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication
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