On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets

Descripción del Articulo

Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, ther...

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Detalles Bibliográficos
Autores: Ocsa, Alexander, Huillca, Jose Luis, Lopez del Alamo, Cristian
Formato: capítulo de libro
Fecha de Publicación:2018
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/1310
Enlace del recurso:https://hdl.handle.net/20.500.12390/1310
https://doi.org/10.1007/978-3-319-75193-1_54
Nivel de acceso:acceso abierto
Materia:Multidimensional index
Approximate similarity search
Fractal theory
Deep learning
https://purl.org/pe-repo/ocde/ford#5.08.02
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spelling Publicationrp03688500rp03692500rp00298500Ocsa, AlexanderHuillca, Jose LuisLopez del Alamo, Cristian2024-05-30T23:13:38Z2024-05-30T23:13:38Z2018https://hdl.handle.net/20.500.12390/1310https://doi.org/10.1007/978-3-319-75193-1_54Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer International PublishingProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications; Lecture Notes in Computer Scienceinfo:eu-repo/semantics/openAccessMultidimensional indexApproximate similarity search-1Fractal theory-1Deep learning-1https://purl.org/pe-repo/ocde/ford#5.08.02-1On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasetsinfo:eu-repo/semantics/bookPartreponame: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#20.500.12390/1310oai:repositorio.concytec.gob.pe:20.500.12390/13102024-05-30 15:52:00.913http://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#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="db5590ab-40da-4048-b48f-5d384a7060b6"> <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>On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets</Title> <PublishedIn> <Publication> <Title>Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications; Lecture Notes in Computer Science</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1007/978-3-319-75193-1_54</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>Lopez del Alamo, Cristian</DisplayName> <Person id="rp00298" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer International Publishing</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Multidimensional index</Keyword> <Keyword>Approximate similarity search</Keyword> <Keyword>Fractal theory</Keyword> <Keyword>Deep learning</Keyword> <Abstract>Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
title On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
spellingShingle On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
Ocsa, Alexander
Multidimensional index
Approximate similarity search
Fractal theory
Deep learning
https://purl.org/pe-repo/ocde/ford#5.08.02
title_short On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
title_full On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
title_fullStr On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
title_full_unstemmed On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
title_sort On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
author Ocsa, Alexander
author_facet Ocsa, Alexander
Huillca, Jose Luis
Lopez del Alamo, Cristian
author_role author
author2 Huillca, Jose Luis
Lopez del Alamo, Cristian
author2_role author
author
dc.contributor.author.fl_str_mv Ocsa, Alexander
Huillca, Jose Luis
Lopez del Alamo, Cristian
dc.subject.none.fl_str_mv Multidimensional index
topic Multidimensional index
Approximate similarity search
Fractal theory
Deep learning
https://purl.org/pe-repo/ocde/ford#5.08.02
dc.subject.es_PE.fl_str_mv Approximate similarity search
Fractal theory
Deep learning
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.08.02
description Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters.
publishDate 2018
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 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/1310
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-319-75193-1_54
url https://hdl.handle.net/20.500.12390/1310
https://doi.org/10.1007/978-3-319-75193-1_54
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications; Lecture Notes in Computer Science
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer International Publishing
publisher.none.fl_str_mv Springer International Publishing
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
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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score 13.434648
On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
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).

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