Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10400.6/9177 |
Resumo: | Markov Random Fields (MRFs) are a populartool in many computer vision problems and faithfully modela broad range of local dependencies. However, rooted in theHammersley-Clifford theorem, they face serious difficulties inenforcing the global coherence of the solutions without using toohigh order cliques that reduce the computational effectiveness ofthe inference phase. Having this problem in mind, we describea multi-layered (hierarchical) architecture for MRFs that isbased exclusively in pairwise connections and typically producesglobally coherent solutions, with 1) one layer working at the local(pixel) level, modelling the interactions between adjacent imagepatches; and 2) a complementary layer working at theobject(hypothesis) level pushing toward globally consistent solutions.During optimization, both layers interact into an equilibriumstate, that not only segments the data, but also classifies it.The proposed MRF architecture is particularly suitable forproblems that deal with biological data (e.g., biometrics), wherethe reasonability of the solutions can be objectively measured.As test case, we considered the problem of hair / facial hairsegmentation and labelling, which are soft biometric labels usefulfor human recognitionin-the-wild. We observed performancelevels close to the state-of-the-art at a much lower computationalcost, both in the segmentation and classification (labelling) tasks |
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7160 |
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Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFsHair analysisSoft BiometricsVisual SurveillanceHomeland SecurityMarkov Random Fields (MRFs) are a populartool in many computer vision problems and faithfully modela broad range of local dependencies. However, rooted in theHammersley-Clifford theorem, they face serious difficulties inenforcing the global coherence of the solutions without using toohigh order cliques that reduce the computational effectiveness ofthe inference phase. Having this problem in mind, we describea multi-layered (hierarchical) architecture for MRFs that isbased exclusively in pairwise connections and typically producesglobally coherent solutions, with 1) one layer working at the local(pixel) level, modelling the interactions between adjacent imagepatches; and 2) a complementary layer working at theobject(hypothesis) level pushing toward globally consistent solutions.During optimization, both layers interact into an equilibriumstate, that not only segments the data, but also classifies it.The proposed MRF architecture is particularly suitable forproblems that deal with biological data (e.g., biometrics), wherethe reasonability of the solutions can be objectively measured.As test case, we considered the problem of hair / facial hairsegmentation and labelling, which are soft biometric labels usefulfor human recognitionin-the-wild. We observed performancelevels close to the state-of-the-art at a much lower computationalcost, both in the segmentation and classification (labelling) tasksuBibliorumProença, H.Neves, Joao2020-02-10T14:48:56Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9177eng10.1109/TIFS.2017.2680246info: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:RCAAP2023-12-15T09:49:50Zoai:ubibliorum.ubi.pt:10400.6/9177Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:20.781619Repositório 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 |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
title |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
spellingShingle |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs Proença, H. Hair analysis Soft Biometrics Visual Surveillance Homeland Security |
title_short |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
title_full |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
title_fullStr |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
title_full_unstemmed |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
title_sort |
Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs |
author |
Proença, H. |
author_facet |
Proença, H. Neves, Joao |
author_role |
author |
author2 |
Neves, Joao |
author2_role |
author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Proença, H. Neves, Joao |
dc.subject.por.fl_str_mv |
Hair analysis Soft Biometrics Visual Surveillance Homeland Security |
topic |
Hair analysis Soft Biometrics Visual Surveillance Homeland Security |
description |
Markov Random Fields (MRFs) are a populartool in many computer vision problems and faithfully modela broad range of local dependencies. However, rooted in theHammersley-Clifford theorem, they face serious difficulties inenforcing the global coherence of the solutions without using toohigh order cliques that reduce the computational effectiveness ofthe inference phase. Having this problem in mind, we describea multi-layered (hierarchical) architecture for MRFs that isbased exclusively in pairwise connections and typically producesglobally coherent solutions, with 1) one layer working at the local(pixel) level, modelling the interactions between adjacent imagepatches; and 2) a complementary layer working at theobject(hypothesis) level pushing toward globally consistent solutions.During optimization, both layers interact into an equilibriumstate, that not only segments the data, but also classifies it.The proposed MRF architecture is particularly suitable forproblems that deal with biological data (e.g., biometrics), wherethe reasonability of the solutions can be objectively measured.As test case, we considered the problem of hair / facial hairsegmentation and labelling, which are soft biometric labels usefulfor human recognitionin-the-wild. We observed performancelevels close to the state-of-the-art at a much lower computationalcost, both in the segmentation and classification (labelling) tasks |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z 2020-02-10T14:48:56Z |
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/10400.6/9177 |
url |
http://hdl.handle.net/10400.6/9177 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/TIFS.2017.2680246 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136385862467584 |