Unsupervised edge map scoring: A statistical complexity approach

Autores
Gimenez Romero, Javier Alejandro; Martinez, Jorge Alberto; Flesia, Ana Georgina
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a new Statistical Complexity Measure (SCM) to qualify edge maps without Ground Truth (GT) knowledge. The measure is the product of two indices, an Equilibrium index E obtained by projecting the edge map into a family of edge patterns, and an Entropy index H, defined as a function of the Kolmogorov–Smirnov (KS) statistic. This new measure can be used for performance characterization which includes: (i) the specific evaluation of an algorithm (intra-technique process) in order to identify its best parameters and (ii) the comparison of different algorithms (inter-technique process) in order to classify them according to their quality. Results made over images of the South Florida and Berkeley databases show that our approach significantly improves over Pratt’s Figure of Merit (PFoM) which is the objective reference-based edge map evaluation standard, as it takes into account more features in its evaluation.
Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Jorge Alberto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Unsupervised Quality Measure
Edge Maps
Statistical Complexity
Edge Patterns
Entropy
Kolmogorov–Smirnov Statistic
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/32174
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Unsupervised edge map scoring: A statistical complexity approachGimenez Romero, Javier AlejandroMartinez, Jorge AlbertoFlesia, Ana GeorginaUnsupervised Quality MeasureEdge MapsStatistical ComplexityEdge PatternsEntropyKolmogorov–Smirnov Statistichttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We propose a new Statistical Complexity Measure (SCM) to qualify edge maps without Ground Truth (GT) knowledge. The measure is the product of two indices, an Equilibrium index E obtained by projecting the edge map into a family of edge patterns, and an Entropy index H, defined as a function of the Kolmogorov–Smirnov (KS) statistic. This new measure can be used for performance characterization which includes: (i) the specific evaluation of an algorithm (intra-technique process) in order to identify its best parameters and (ii) the comparison of different algorithms (inter-technique process) in order to classify them according to their quality. Results made over images of the South Florida and Berkeley databases show that our approach significantly improves over Pratt’s Figure of Merit (PFoM) which is the objective reference-based edge map evaluation standard, as it takes into account more features in its evaluation.Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Jorge Alberto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAcademic Press Inc Elsevier Science2014-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/32174Gimenez Romero, Javier Alejandro; Flesia, Ana Georgina; Martinez, Jorge Alberto; Unsupervised edge map scoring: A statistical complexity approach; Academic Press Inc Elsevier Science; Computer Vision And Image Understanding; 122; 3-2014; 131-1421077-3142CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cviu.2014.02.005info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1077314214000319info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:02:03Zoai:ri.conicet.gov.ar:11336/32174instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:02:03.619CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Unsupervised edge map scoring: A statistical complexity approach
title Unsupervised edge map scoring: A statistical complexity approach
spellingShingle Unsupervised edge map scoring: A statistical complexity approach
Gimenez Romero, Javier Alejandro
Unsupervised Quality Measure
Edge Maps
Statistical Complexity
Edge Patterns
Entropy
Kolmogorov–Smirnov Statistic
title_short Unsupervised edge map scoring: A statistical complexity approach
title_full Unsupervised edge map scoring: A statistical complexity approach
title_fullStr Unsupervised edge map scoring: A statistical complexity approach
title_full_unstemmed Unsupervised edge map scoring: A statistical complexity approach
title_sort Unsupervised edge map scoring: A statistical complexity approach
dc.creator.none.fl_str_mv Gimenez Romero, Javier Alejandro
Martinez, Jorge Alberto
Flesia, Ana Georgina
author Gimenez Romero, Javier Alejandro
author_facet Gimenez Romero, Javier Alejandro
Martinez, Jorge Alberto
Flesia, Ana Georgina
author_role author
author2 Martinez, Jorge Alberto
Flesia, Ana Georgina
author2_role author
author
dc.subject.none.fl_str_mv Unsupervised Quality Measure
Edge Maps
Statistical Complexity
Edge Patterns
Entropy
Kolmogorov–Smirnov Statistic
topic Unsupervised Quality Measure
Edge Maps
Statistical Complexity
Edge Patterns
Entropy
Kolmogorov–Smirnov Statistic
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose a new Statistical Complexity Measure (SCM) to qualify edge maps without Ground Truth (GT) knowledge. The measure is the product of two indices, an Equilibrium index E obtained by projecting the edge map into a family of edge patterns, and an Entropy index H, defined as a function of the Kolmogorov–Smirnov (KS) statistic. This new measure can be used for performance characterization which includes: (i) the specific evaluation of an algorithm (intra-technique process) in order to identify its best parameters and (ii) the comparison of different algorithms (inter-technique process) in order to classify them according to their quality. Results made over images of the South Florida and Berkeley databases show that our approach significantly improves over Pratt’s Figure of Merit (PFoM) which is the objective reference-based edge map evaluation standard, as it takes into account more features in its evaluation.
Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Jorge Alberto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We propose a new Statistical Complexity Measure (SCM) to qualify edge maps without Ground Truth (GT) knowledge. The measure is the product of two indices, an Equilibrium index E obtained by projecting the edge map into a family of edge patterns, and an Entropy index H, defined as a function of the Kolmogorov–Smirnov (KS) statistic. This new measure can be used for performance characterization which includes: (i) the specific evaluation of an algorithm (intra-technique process) in order to identify its best parameters and (ii) the comparison of different algorithms (inter-technique process) in order to classify them according to their quality. Results made over images of the South Florida and Berkeley databases show that our approach significantly improves over Pratt’s Figure of Merit (PFoM) which is the objective reference-based edge map evaluation standard, as it takes into account more features in its evaluation.
publishDate 2014
dc.date.none.fl_str_mv 2014-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/32174
Gimenez Romero, Javier Alejandro; Flesia, Ana Georgina; Martinez, Jorge Alberto; Unsupervised edge map scoring: A statistical complexity approach; Academic Press Inc Elsevier Science; Computer Vision And Image Understanding; 122; 3-2014; 131-142
1077-3142
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32174
identifier_str_mv Gimenez Romero, Javier Alejandro; Flesia, Ana Georgina; Martinez, Jorge Alberto; Unsupervised edge map scoring: A statistical complexity approach; Academic Press Inc Elsevier Science; Computer Vision And Image Understanding; 122; 3-2014; 131-142
1077-3142
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cviu.2014.02.005
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1077314214000319
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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