Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering

Descripción del Articulo

The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnolog ıa e Innovacion Tecnoloogica ), FONDE- ´ CYT (Fondo Nacional de Desarrollo Cient´ıfico y Tecnologico) ´and ANA (Autoridad Nacional del Agua) for satellite imagesand supporting this project
Detalles Bibliográficos
Autores: Mantilla S.C.L., Yari Y.
Formato: objeto de conferencia
Fecha de Publicación:2018
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/705
Enlace del recurso:https://hdl.handle.net/20.500.12390/705
https://doi.org/10.1109/LA-CCI.2017.8285729
Nivel de acceso:acceso abierto
Materia:Weight information
Artificial intelligence
Classification (of information)
Pattern recognition
Gaussian dispersions
Multispectral images
Objective functions
Satellite images
Similarity between objects
Unsupervised classification
Unsupervised clustering
Image segmentation
https://purl.org/pe-repo/ocde/ford#1.02.00
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/705
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
title Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
spellingShingle Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
Mantilla S.C.L.
Weight information
Artificial intelligence
Classification (of information)
Pattern recognition
Gaussian dispersions
Multispectral images
Objective functions
Satellite images
Similarity between objects
Unsupervised classification
Unsupervised clustering
Image segmentation
https://purl.org/pe-repo/ocde/ford#1.02.00
title_short Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
title_full Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
title_fullStr Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
title_full_unstemmed Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
title_sort Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
author Mantilla S.C.L.
author_facet Mantilla S.C.L.
Yari Y.
author_role author
author2 Yari Y.
author2_role author
dc.contributor.author.fl_str_mv Mantilla S.C.L.
Yari Y.
dc.subject.none.fl_str_mv Weight information
topic Weight information
Artificial intelligence
Classification (of information)
Pattern recognition
Gaussian dispersions
Multispectral images
Objective functions
Satellite images
Similarity between objects
Unsupervised classification
Unsupervised clustering
Image segmentation
https://purl.org/pe-repo/ocde/ford#1.02.00
dc.subject.es_PE.fl_str_mv Artificial intelligence
Classification (of information)
Pattern recognition
Gaussian dispersions
Multispectral images
Objective functions
Satellite images
Similarity between objects
Unsupervised classification
Unsupervised clustering
Image segmentation
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.00
description The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnolog ıa e Innovacion Tecnoloogica ), FONDE- ´ CYT (Fondo Nacional de Desarrollo Cient´ıfico y Tecnologico) ´and ANA (Autoridad Nacional del Agua) for satellite imagesand supporting this project
publishDate 2018
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 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.isbn.none.fl_str_mv 9781538637340
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/705
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/LA-CCI.2017.8285729
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85050374001
identifier_str_mv 9781538637340
2-s2.0-85050374001
url https://hdl.handle.net/20.500.12390/705
https://doi.org/10.1109/LA-CCI.2017.8285729
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers 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
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spelling Publicationrp01717600rp01718600Mantilla S.C.L.Yari Y.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20189781538637340https://hdl.handle.net/20.500.12390/705https://doi.org/10.1109/LA-CCI.2017.82857292-s2.0-85050374001The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnolog ıa e Innovacion Tecnoloogica ), FONDE- ´ CYT (Fondo Nacional de Desarrollo Cient´ıfico y Tecnologico) ´and ANA (Autoridad Nacional del Agua) for satellite imagesand supporting this projectIn Pattern Recognition there are many algorithms it try to solve the problem of grouping objects of the same type, this is called clustering, however the task of dividing these lies not only in the objective function, but also the methodology used to calculate the similarity between objects. Because multispectral images contain information that has low statistical separation and a large amount of data it is necessary to enter local information. In this paper, the use of the Gaussian dispersion equation is proposed in order to calculate the contribution of each sample to the sample analyzed. The results show that the integration of local weights within the clustering model decreases the entropy of each group generated.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengInstitute of Electrical and Electronics Engineers Inc.2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedingsinfo:eu-repo/semantics/openAccessWeight informationArtificial intelligence-1Classification (of information)-1Pattern recognition-1Gaussian dispersions-1Multispectral images-1Objective functions-1Satellite images-1Similarity between objects-1Unsupervised classification-1Unsupervised clustering-1Image segmentation-1https://purl.org/pe-repo/ocde/ford#1.02.00-1Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clusteringinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/705oai:repositorio.concytec.gob.pe:20.500.12390/7052024-05-30 15:22:41.97http://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#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cf7d202e-ea7d-4f84-aee8-03efce7e1d49"> <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>Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering</Title> <PublishedIn> <Publication> <Title>2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1109/LA-CCI.2017.8285729</DOI> <SCP-Number>2-s2.0-85050374001</SCP-Number> <ISBN>9781538637340</ISBN> <Authors> <Author> <DisplayName>Mantilla S.C.L.</DisplayName> <Person id="rp01717" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Yari Y.</DisplayName> <Person id="rp01718" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Institute of Electrical and Electronics Engineers Inc.</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Weight information</Keyword> <Keyword>Artificial intelligence</Keyword> <Keyword>Classification (of information)</Keyword> <Keyword>Pattern recognition</Keyword> <Keyword>Gaussian dispersions</Keyword> <Keyword>Multispectral images</Keyword> <Keyword>Objective functions</Keyword> <Keyword>Satellite images</Keyword> <Keyword>Similarity between objects</Keyword> <Keyword>Unsupervised classification</Keyword> <Keyword>Unsupervised clustering</Keyword> <Keyword>Image segmentation</Keyword> <Abstract>In Pattern Recognition there are many algorithms it try to solve the problem of grouping objects of the same type, this is called clustering, however the task of dividing these lies not only in the objective function, but also the methodology used to calculate the similarity between objects. Because multispectral images contain information that has low statistical separation and a large amount of data it is necessary to enter local information. In this paper, the use of the Gaussian dispersion equation is proposed in order to calculate the contribution of each sample to the sample analyzed. The results show that the integration of local weights within the clustering model decreases the entropy of each group generated.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.402391
Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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