Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks

Autores
Bouza, Magdalena; Cernuschi Frías, Bruno
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x1) between the nodes X of the network N = (X;Ax). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/52132
Acceder
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network_name_str SEDICI (UNLP)
spelling Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric NetworksBouza, MagdalenaCernuschi Frías, BrunoCiencias InformáticasClusteringPATTERN RECOGNITIONIMAGE PROCESSING AND COMPUTER VISIONIn this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x<sup>1</sup>) between the nodes X of the network N = (X;A<sub>x</sub>). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2015info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf192-199http://sedici.unlp.edu.ar/handle/10915/52132enginfo:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/asai192-199.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:37:09Zoai:sedici.unlp.edu.ar:10915/52132Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:37:09.924SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
title Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
spellingShingle Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
Bouza, Magdalena
Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
title_short Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
title_full Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
title_fullStr Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
title_full_unstemmed Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
title_sort Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
dc.creator.none.fl_str_mv Bouza, Magdalena
Cernuschi Frías, Bruno
author Bouza, Magdalena
author_facet Bouza, Magdalena
Cernuschi Frías, Bruno
author_role author
author2 Cernuschi Frías, Bruno
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
topic Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
dc.description.none.fl_txt_mv In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x<sup>1</sup>) between the nodes X of the network N = (X;A<sub>x</sub>). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x<sup>1</sup>) between the nodes X of the network N = (X;A<sub>x</sub>). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/52132
url http://sedici.unlp.edu.ar/handle/10915/52132
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/asai192-199.pdf
info:eu-repo/semantics/altIdentifier/issn/2451-7585
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
192-199
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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