Multilayer complex network descriptors for color–texture characterization

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L. F. S. Scabini acknowledges support from CNPq (Grants #134558/2016-2 and #142438/2018-9). O. M. Bruno acknowledges support from CNPq (Grant #307797/2014-7 and Grant #484312/2013-8) and FAPESP (grant #14/08026-1 and #16/18809-9). R. H. M. Condori acknowledges support from Cienciactiva, an initiativ...

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Detalles Bibliográficos
Autores: Scabini L.F.S., Condori R.H.M., Gonçalves W.N., Bruno O.M.
Formato: artículo
Fecha de Publicación:2019
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/522
Enlace del recurso:https://hdl.handle.net/20.500.12390/522
https://doi.org/10.1016/j.ins.2019.02.060
Nivel de acceso:acceso abierto
Materia:Threshold selection
Classification (of information)
Color
Convolution
Deep neural networks
Feature extraction
Multilayers
Network layers
Neural networks
Textures
Adaptive approach
Characterization techniques
Convolutional neural network
Multi-layer network
Spatial interaction
Texture analysis
Texture characterizations
Complex networks
https://purl.org/pe-repo/ocde/ford#1.02.02
id CONC_81f352c07749ac9d8b7e14ab2df90e8c
oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/522
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Multilayer complex network descriptors for color–texture characterization
title Multilayer complex network descriptors for color–texture characterization
spellingShingle Multilayer complex network descriptors for color–texture characterization
Scabini L.F.S.
Threshold selection
Classification (of information)
Color
Convolution
Deep neural networks
Feature extraction
Multilayers
Network layers
Neural networks
Neural networks
Textures
Adaptive approach
Characterization techniques
Convolutional neural network
Multi-layer network
Spatial interaction
Texture analysis
Texture characterizations
Complex networks
https://purl.org/pe-repo/ocde/ford#1.02.02
title_short Multilayer complex network descriptors for color–texture characterization
title_full Multilayer complex network descriptors for color–texture characterization
title_fullStr Multilayer complex network descriptors for color–texture characterization
title_full_unstemmed Multilayer complex network descriptors for color–texture characterization
title_sort Multilayer complex network descriptors for color–texture characterization
author Scabini L.F.S.
author_facet Scabini L.F.S.
Condori R.H.M.
Gonçalves W.N.
Bruno O.M.
author_role author
author2 Condori R.H.M.
Gonçalves W.N.
Bruno O.M.
author2_role author
author
author
dc.contributor.author.fl_str_mv Scabini L.F.S.
Condori R.H.M.
Gonçalves W.N.
Bruno O.M.
dc.subject.none.fl_str_mv Threshold selection
topic Threshold selection
Classification (of information)
Color
Convolution
Deep neural networks
Feature extraction
Multilayers
Network layers
Neural networks
Neural networks
Textures
Adaptive approach
Characterization techniques
Convolutional neural network
Multi-layer network
Spatial interaction
Texture analysis
Texture characterizations
Complex networks
https://purl.org/pe-repo/ocde/ford#1.02.02
dc.subject.es_PE.fl_str_mv Classification (of information)
Color
Convolution
Deep neural networks
Feature extraction
Multilayers
Network layers
Neural networks
Neural networks
Textures
Adaptive approach
Characterization techniques
Convolutional neural network
Multi-layer network
Spatial interaction
Texture analysis
Texture characterizations
Complex networks
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.02
description L. F. S. Scabini acknowledges support from CNPq (Grants #134558/2016-2 and #142438/2018-9). O. M. Bruno acknowledges support from CNPq (Grant #307797/2014-7 and Grant #484312/2013-8) and FAPESP (grant #14/08026-1 and #16/18809-9). R. H. M. Condori acknowledges support from Cienciactiva, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru). W. N. Gonçalves acknowledges support from CNPq (Grant #304173/2016-9) and Fundect (Grant #071/2015). The authors are grateful to Abdelmounaime Safia for the feedback concerning the MBT dataset construction, and the NVIDIA GPU Grant Program for the donation of the Quadro P6000 and the Titan Xp GPUs used on this research.
publishDate 2019
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 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/522
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.ins.2019.02.060
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85063901933
url https://hdl.handle.net/20.500.12390/522
https://doi.org/10.1016/j.ins.2019.02.060
identifier_str_mv 2-s2.0-85063901933
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Information Sciences
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier Inc.
publisher.none.fl_str_mv Elsevier Inc.
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
_version_ 1839175720444624896
spelling Publicationrp00843600rp00841600rp00844600rp00842600Scabini L.F.S.Condori R.H.M.Gonçalves W.N.Bruno O.M.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2019https://hdl.handle.net/20.500.12390/522https://doi.org/10.1016/j.ins.2019.02.0602-s2.0-85063901933L. F. S. Scabini acknowledges support from CNPq (Grants #134558/2016-2 and #142438/2018-9). O. M. Bruno acknowledges support from CNPq (Grant #307797/2014-7 and Grant #484312/2013-8) and FAPESP (grant #14/08026-1 and #16/18809-9). R. H. M. Condori acknowledges support from Cienciactiva, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru). W. N. Gonçalves acknowledges support from CNPq (Grant #304173/2016-9) and Fundect (Grant #071/2015). The authors are grateful to Abdelmounaime Safia for the feedback concerning the MBT dataset construction, and the NVIDIA GPU Grant Program for the donation of the Quadro P6000 and the Titan Xp GPUs used on this research.A new method based on complex networks is proposed for color–texture analysis. The proposal consists of modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet, and MBT. Results among various literature methods are compared, including deep convolutional neural networks. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengElsevier Inc.Information Sciencesinfo:eu-repo/semantics/openAccessThreshold selectionClassification (of information)-1Color-1Convolution-1Deep neural networks-1Feature extraction-1Multilayers-1Network layers-1Neural networks-1Neural networks-1Textures-1Adaptive approach-1Characterization techniques-1Convolutional neural network-1Multi-layer network-1Spatial interaction-1Texture analysis-1Texture characterizations-1Complex networks-1https://purl.org/pe-repo/ocde/ford#1.02.02-1Multilayer complex network descriptors for color–texture characterizationinfo:eu-repo/semantics/articlereponame: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#20.500.12390/522oai:repositorio.concytec.gob.pe:20.500.12390/5222024-05-30 15:22:05.231http://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##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b85e4730-ecc8-4cf3-bfef-6687b34a9f12"> <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>Multilayer complex network descriptors for color–texture characterization</Title> <PublishedIn> <Publication> <Title>Information Sciences</Title> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <DOI>https://doi.org/10.1016/j.ins.2019.02.060</DOI> <SCP-Number>2-s2.0-85063901933</SCP-Number> <Authors> <Author> <DisplayName>Scabini L.F.S.</DisplayName> <Person id="rp00843" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Condori R.H.M.</DisplayName> <Person id="rp00841" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Gonçalves W.N.</DisplayName> <Person id="rp00844" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Bruno O.M.</DisplayName> <Person id="rp00842" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Elsevier Inc.</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Threshold selection</Keyword> <Keyword>Classification (of information)</Keyword> <Keyword>Color</Keyword> <Keyword>Convolution</Keyword> <Keyword>Deep neural networks</Keyword> <Keyword>Feature extraction</Keyword> <Keyword>Multilayers</Keyword> <Keyword>Network layers</Keyword> <Keyword>Neural networks</Keyword> <Keyword>Neural networks</Keyword> <Keyword>Textures</Keyword> <Keyword>Adaptive approach</Keyword> <Keyword>Characterization techniques</Keyword> <Keyword>Convolutional neural network</Keyword> <Keyword>Multi-layer network</Keyword> <Keyword>Spatial interaction</Keyword> <Keyword>Texture analysis</Keyword> <Keyword>Texture characterizations</Keyword> <Keyword>Complex networks</Keyword> <Abstract>A new method based on complex networks is proposed for color–texture analysis. The proposal consists of modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet, and MBT. Results among various literature methods are compared, including deep convolutional neural networks. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.441895
Multilayer complex network descriptors for color–texture characterization
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