Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition based on Hierarchical MRFs

Detalhes bibliográficos
Autor(a) principal: Proença, H.
Data de Publicação: 2017
Outros Autores: Neves, Joao
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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spelling 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
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dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1109/TIFS.2017.2680246
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