Unsupervised similarity learning through rank correlation and kNN sets
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1145/3241053 http://hdl.handle.net/11449/187328 |
Resumo: | The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets. |
id |
UNSP_21770a36f99843e7e8ef9144b3c53a6c |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/187328 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Unsupervised similarity learning through rank correlation and kNN setsContent-based image retrievalKNN setsRank correlationUnsupervised learningThe increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets.Department of Statistics Applied Mathematics and Computing São Paulo State University - UNESP, Av. 24-A, 1515Instituto de Ciência e Tecnologia Universidade Federal de São Paulo - UNIFESP, Av. Cesare M. G. Lattes, 1201Department of Statistics Applied Mathematics and Computing São Paulo State University - UNESP, Av. 24-A, 1515Universidade Estadual Paulista (Unesp)Universidade Federal de São Paulo (UNIFESP)Valem, Lucas Pascotti [UNESP]De Oliveira, Carlos Renan [UNESP]Pedronette, Daniel Carlos Guimarães [UNESP]Almeida, Jurandy2019-10-06T15:32:44Z2019-10-06T15:32:44Z2018-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1145/3241053ACM Transactions on Multimedia Computing, Communications and Applications, v. 14, n. 4, 2018.1551-68651551-6857http://hdl.handle.net/11449/18732810.1145/32410532-s2.0-85061196963Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengACM Transactions on Multimedia Computing, Communications and Applicationsinfo:eu-repo/semantics/openAccess2021-10-22T18:33:44Zoai:repositorio.unesp.br:11449/187328Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:08:07.169899Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Unsupervised similarity learning through rank correlation and kNN sets |
title |
Unsupervised similarity learning through rank correlation and kNN sets |
spellingShingle |
Unsupervised similarity learning through rank correlation and kNN sets Valem, Lucas Pascotti [UNESP] Content-based image retrieval KNN sets Rank correlation Unsupervised learning |
title_short |
Unsupervised similarity learning through rank correlation and kNN sets |
title_full |
Unsupervised similarity learning through rank correlation and kNN sets |
title_fullStr |
Unsupervised similarity learning through rank correlation and kNN sets |
title_full_unstemmed |
Unsupervised similarity learning through rank correlation and kNN sets |
title_sort |
Unsupervised similarity learning through rank correlation and kNN sets |
author |
Valem, Lucas Pascotti [UNESP] |
author_facet |
Valem, Lucas Pascotti [UNESP] De Oliveira, Carlos Renan [UNESP] Pedronette, Daniel Carlos Guimarães [UNESP] Almeida, Jurandy |
author_role |
author |
author2 |
De Oliveira, Carlos Renan [UNESP] Pedronette, Daniel Carlos Guimarães [UNESP] Almeida, Jurandy |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Paulo (UNIFESP) |
dc.contributor.author.fl_str_mv |
Valem, Lucas Pascotti [UNESP] De Oliveira, Carlos Renan [UNESP] Pedronette, Daniel Carlos Guimarães [UNESP] Almeida, Jurandy |
dc.subject.por.fl_str_mv |
Content-based image retrieval KNN sets Rank correlation Unsupervised learning |
topic |
Content-based image retrieval KNN sets Rank correlation Unsupervised learning |
description |
The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-01 2019-10-06T15:32:44Z 2019-10-06T15:32:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1145/3241053 ACM Transactions on Multimedia Computing, Communications and Applications, v. 14, n. 4, 2018. 1551-6865 1551-6857 http://hdl.handle.net/11449/187328 10.1145/3241053 2-s2.0-85061196963 |
url |
http://dx.doi.org/10.1145/3241053 http://hdl.handle.net/11449/187328 |
identifier_str_mv |
ACM Transactions on Multimedia Computing, Communications and Applications, v. 14, n. 4, 2018. 1551-6865 1551-6857 10.1145/3241053 2-s2.0-85061196963 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ACM Transactions on Multimedia Computing, Communications and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129493446950912 |