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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/4806
Ver los metadatos del registro completo
id |
CONICETDig_3fdd06290e6d732cbc9770c3ec216643 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/4806 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
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 |
_version_ |
1846083264018644992 |
score |
13.22299 |