Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance
Descripción del Articulo
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. Rec...
| Autores: | , , , , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2017 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/1273 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/1273 https://doi.org/10.1109/la-cci.2017.8285730 |
| Nivel de acceso: | acceso abierto |
| Materia: | feedforward neural nets convolution data structures https://purl.org/pe-repo/ocde/ford#5.08.02 |
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CONCYTEC-Institucional |
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| dc.title.none.fl_str_mv |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| title |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| spellingShingle |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance Ocsa, Alexander feedforward neural nets convolution data structures https://purl.org/pe-repo/ocde/ford#5.08.02 |
| title_short |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| title_full |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| title_fullStr |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| title_full_unstemmed |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| title_sort |
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance |
| author |
Ocsa, Alexander |
| author_facet |
Ocsa, Alexander Huillca, Jose Luis Coronado, Ricardo Quispe, Oscar Arbieto, Carlos Lopez, Cristian |
| author_role |
author |
| author2 |
Huillca, Jose Luis Coronado, Ricardo Quispe, Oscar Arbieto, Carlos Lopez, Cristian |
| author2_role |
author author author author author |
| dc.contributor.author.fl_str_mv |
Ocsa, Alexander Huillca, Jose Luis Coronado, Ricardo Quispe, Oscar Arbieto, Carlos Lopez, Cristian |
| dc.subject.none.fl_str_mv |
feedforward neural nets |
| topic |
feedforward neural nets convolution data structures https://purl.org/pe-repo/ocde/ford#5.08.02 |
| dc.subject.es_PE.fl_str_mv |
convolution data structures |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#5.08.02 |
| description |
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. |
| publishDate |
2017 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2017-11 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| format |
conferenceObject |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/1273 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1109/la-cci.2017.8285730 |
| url |
https://hdl.handle.net/20.500.12390/1273 https://doi.org/10.1109/la-cci.2017.8285730 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
IEEE |
| publisher.none.fl_str_mv |
IEEE |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
| instname_str |
Consejo Nacional de Ciencia Tecnología e Innovación |
| instacron_str |
CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
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1839175422285185024 |
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Publicationrp03688600rp03692600rp03687600rp03689600rp03690600rp03691600Ocsa, AlexanderHuillca, Jose LuisCoronado, RicardoQuispe, OscarArbieto, CarlosLopez, Cristian2024-05-30T23:13:38Z2024-05-30T23:13:38Z2017-11https://hdl.handle.net/20.500.12390/1273https://doi.org/10.1109/la-cci.2017.8285730The 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.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEE2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)info:eu-repo/semantics/openAccessfeedforward neural netsconvolution-1data structures-1https://purl.org/pe-repo/ocde/ford#5.08.02-1Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performanceinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/1273oai:repositorio.concytec.gob.pe:20.500.12390/12732024-05-30 15:51:49.798http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="12080f15-9c00-4f00-ab1d-4cab4f69aed0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance</Title> <PublishedIn> <Publication> <Title>2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)</Title> </Publication> </PublishedIn> <PublicationDate>2017-11</PublicationDate> <DOI>https://doi.org/10.1109/la-cci.2017.8285730</DOI> <Authors> <Author> <DisplayName>Ocsa, Alexander</DisplayName> <Person id="rp03688" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Huillca, Jose Luis</DisplayName> <Person id="rp03692" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Coronado, Ricardo</DisplayName> <Person id="rp03687" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Quispe, Oscar</DisplayName> <Person id="rp03689" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Arbieto, Carlos</DisplayName> <Person id="rp03690" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Lopez, Cristian</DisplayName> <Person id="rp03691" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>feedforward neural nets</Keyword> <Keyword>convolution</Keyword> <Keyword>data structures</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
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13.448654 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
