Hybrid Consensus Learning for Legume Species and Cultivars Classification

Autores
Larese, Monica Graciela; Granitto, Pablo Miguel
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we propose an automatic method aimed at classifying five legume species and varieties using leaf venation features. Firstly, we segment the leaf veins and measure several multiscale morphological features on the vein segments and the areoles. Next, we build a hybrid consensus of experts formed by five different automatic classifiers to perform the classification using the extracted features. We propose to use two strategies in order to assign the importance to the votes of the algorithms in the consensus. The first one is considering all the algorithms equally important. The second one is based on the accuracy of the standalone classifiers. The performance of both consensus classifiers show to outperform the standalone classification algorithms in the five class recognition task.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Materia
Legume And Variety Classification
Venation Images
Consensus Learning
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:11336/4806

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network_name_str CONICET Digital (CONICET)
spelling Hybrid Consensus Learning for Legume Species and Cultivars ClassificationLarese, Monica GracielaGranitto, Pablo MiguelLegume And Variety ClassificationVenation ImagesConsensus Learninghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work we propose an automatic method aimed at classifying five legume species and varieties using leaf venation features. Firstly, we segment the leaf veins and measure several multiscale morphological features on the vein segments and the areoles. Next, we build a hybrid consensus of experts formed by five different automatic classifiers to perform the classification using the extracted features. We propose to use two strategies in order to assign the importance to the votes of the algorithms in the consensus. The first one is considering all the algorithms equally important. The second one is based on the accuracy of the standalone classifiers. The performance of both consensus classifiers show to outperform the standalone classification algorithms in the five class recognition task.Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaSpringer2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4806Larese, Monica Graciela; Granitto, Pablo Miguel; Hybrid Consensus Learning for Legume Species and Cultivars Classification; Springer; Computer Vision - ECCV 2014 Workshops; 8928; 3-2015; 201-214978-3-319-16219-50302-9743enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007%2F978-3-319-16220-1_15info:eu-repo/semantics/altIdentifier/isbn/978-3-319-16219-5info:eu-repo/semantics/altIdentifier/issn/0302-9743info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-16220-1_15info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:11:35Zoai:ri.conicet.gov.ar:11336/4806instacron: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:34982025-10-15 15:11:35.577CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hybrid Consensus Learning for Legume Species and Cultivars Classification
title Hybrid Consensus Learning for Legume Species and Cultivars Classification
spellingShingle Hybrid Consensus Learning for Legume Species and Cultivars Classification
Larese, Monica Graciela
Legume And Variety Classification
Venation Images
Consensus Learning
title_short Hybrid Consensus Learning for Legume Species and Cultivars Classification
title_full Hybrid Consensus Learning for Legume Species and Cultivars Classification
title_fullStr Hybrid Consensus Learning for Legume Species and Cultivars Classification
title_full_unstemmed Hybrid Consensus Learning for Legume Species and Cultivars Classification
title_sort Hybrid Consensus Learning for Legume Species and Cultivars Classification
dc.creator.none.fl_str_mv Larese, Monica Graciela
Granitto, Pablo Miguel
author Larese, Monica Graciela
author_facet Larese, Monica Graciela
Granitto, Pablo Miguel
author_role author
author2 Granitto, Pablo Miguel
author2_role author
dc.subject.none.fl_str_mv Legume And Variety Classification
Venation Images
Consensus Learning
topic Legume And Variety Classification
Venation Images
Consensus Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work we propose an automatic method aimed at classifying five legume species and varieties using leaf venation features. Firstly, we segment the leaf veins and measure several multiscale morphological features on the vein segments and the areoles. Next, we build a hybrid consensus of experts formed by five different automatic classifiers to perform the classification using the extracted features. We propose to use two strategies in order to assign the importance to the votes of the algorithms in the consensus. The first one is considering all the algorithms equally important. The second one is based on the accuracy of the standalone classifiers. The performance of both consensus classifiers show to outperform the standalone classification algorithms in the five class recognition task.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
description In this work we propose an automatic method aimed at classifying five legume species and varieties using leaf venation features. Firstly, we segment the leaf veins and measure several multiscale morphological features on the vein segments and the areoles. Next, we build a hybrid consensus of experts formed by five different automatic classifiers to perform the classification using the extracted features. We propose to use two strategies in order to assign the importance to the votes of the algorithms in the consensus. The first one is considering all the algorithms equally important. The second one is based on the accuracy of the standalone classifiers. The performance of both consensus classifiers show to outperform the standalone classification algorithms in the five class recognition task.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
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/11336/4806
Larese, Monica Graciela; Granitto, Pablo Miguel; Hybrid Consensus Learning for Legume Species and Cultivars Classification; Springer; Computer Vision - ECCV 2014 Workshops; 8928; 3-2015; 201-214
978-3-319-16219-5
0302-9743
url http://hdl.handle.net/11336/4806
identifier_str_mv Larese, Monica Graciela; Granitto, Pablo Miguel; Hybrid Consensus Learning for Legume Species and Cultivars Classification; Springer; Computer Vision - ECCV 2014 Workshops; 8928; 3-2015; 201-214
978-3-319-16219-5
0302-9743
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007%2F978-3-319-16220-1_15
info:eu-repo/semantics/altIdentifier/isbn/978-3-319-16219-5
info:eu-repo/semantics/altIdentifier/issn/0302-9743
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-16220-1_15
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
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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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