Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks

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...

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Detalles Bibliográficos
Autores principales: Bouza, Magdalena, Cernuschi Frías, Bruno
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/52132
http://44jaiio.sadio.org.ar/sites/default/files/asai192-199.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-52132
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
spellingShingle Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
Bouza, Magdalena
Cernuschi Frías, Bruno
Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
topic_facet Ciencias Informáticas
Clustering
PATTERN RECOGNITION
IMAGE PROCESSING AND COMPUTER VISION
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.
format Objeto de conferencia
Objeto de conferencia
author Bouza, Magdalena
Cernuschi Frías, Bruno
author_facet Bouza, Magdalena
Cernuschi Frías, Bruno
author_sort Bouza, Magdalena
title Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks
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
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/52132
http://44jaiio.sadio.org.ar/sites/default/files/asai192-199.pdf
work_keys_str_mv AT bouzamagdalena commonvisualpatternrecognitionusinghierarchicalclusteringmethodswithasymmetricnetworks
AT cernuschifriasbruno commonvisualpatternrecognitionusinghierarchicalclusteringmethodswithasymmetricnetworks
bdutipo_str Repositorios
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