Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/6035 |
Resumo: | In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions. |
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7160 |
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Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approachesRobot visual localizationLocal image featuresInformation fusionMultiple classifier systemsDiscriminativityIn the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.SpringerRCIPLCampos, Francisco M.Correia, LuísCalado, João Manuel Ferreira2016-04-19T12:21:19Z2015-022015-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/6035engCAMPOS, Francisco Marnoto; [et al] - Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches. Journal of Intelligent & Robotics Systems. ISSN 0921-0296. Vol. 77, N.º 2 (2015), pp. 377-3900921-029610.1007/s10846-013-0016-3metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T09:50:18Zoai:repositorio.ipl.pt:10400.21/6035Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:15:14.851629Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
title |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
spellingShingle |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches Campos, Francisco M. Robot visual localization Local image features Information fusion Multiple classifier systems Discriminativity |
title_short |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
title_full |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
title_fullStr |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
title_full_unstemmed |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
title_sort |
Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches |
author |
Campos, Francisco M. |
author_facet |
Campos, Francisco M. Correia, Luís Calado, João Manuel Ferreira |
author_role |
author |
author2 |
Correia, Luís Calado, João Manuel Ferreira |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Campos, Francisco M. Correia, Luís Calado, João Manuel Ferreira |
dc.subject.por.fl_str_mv |
Robot visual localization Local image features Information fusion Multiple classifier systems Discriminativity |
topic |
Robot visual localization Local image features Information fusion Multiple classifier systems Discriminativity |
description |
In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-02 2015-02-01T00:00:00Z 2016-04-19T12:21:19Z |
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://hdl.handle.net/10400.21/6035 |
url |
http://hdl.handle.net/10400.21/6035 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
CAMPOS, Francisco Marnoto; [et al] - Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches. Journal of Intelligent & Robotics Systems. ISSN 0921-0296. Vol. 77, N.º 2 (2015), pp. 377-390 0921-0296 10.1007/s10846-013-0016-3 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133410861514752 |