“You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification

Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researche...

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Detalles Bibliográficos
Autores: Diez Pastor, José Francisco, Latorre Carmona, Pedro, Arnaiz González, Álvar, Ruiz Pérez, Javier, Zurro, Débora
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/7378
Acceso en línea:http://hdl.handle.net/10259/7378
Access Level:acceso abierto
Palabra clave:Feature extraction
Machine learning
Microfossils
Morphometry
Proxy
Informática
Computer science
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spelling “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith IdentificationDiez Pastor, José FranciscoLatorre Carmona, PedroArnaiz González, ÁlvarRuiz Pérez, JavierZurro, DéboraFeature extractionMachine learningMicrofossilsMorphometryProxyInformáticaComputer sciencePhytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.This work was supported by the project TIN2015- 67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, by the project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León (both projects co-financed through European Union FEDER funds) and by Grups de Recerca de Qualitat CaSEs – Culture and Socio-Ecological Dynamics (2017 SGR 212), AGAUR-Generalitat de Catalunya. The authors gratefully acknowledge the support of NVIDIA Corporation and its donation of the TITAN Xp GPUs used in this research.Cambridge University Press202320232020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/7378reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésMicroscopy and Microanalysis. 2020, V. 26, n. 6, p. 1158-1167https://doi.org/10.1017/S1431927620024629info:eu-repo/grantAgreement/MINECO//TIN2015-67534-Pinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17info:eu-repo/grantAgreement/AGAUR//2017 SGR 212Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/73782026-05-28T07:56:11Z
dc.title.none.fl_str_mv “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
title “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
spellingShingle “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
Diez Pastor, José Francisco
Feature extraction
Machine learning
Microfossils
Morphometry
Proxy
Informática
Computer science
title_short “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
title_full “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
title_fullStr “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
title_full_unstemmed “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
title_sort “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification
dc.creator.none.fl_str_mv Diez Pastor, José Francisco
Latorre Carmona, Pedro
Arnaiz González, Álvar
Ruiz Pérez, Javier
Zurro, Débora
author Diez Pastor, José Francisco
author_facet Diez Pastor, José Francisco
Latorre Carmona, Pedro
Arnaiz González, Álvar
Ruiz Pérez, Javier
Zurro, Débora
author_role author
author2 Latorre Carmona, Pedro
Arnaiz González, Álvar
Ruiz Pérez, Javier
Zurro, Débora
author2_role author
author
author
author
dc.subject.none.fl_str_mv Feature extraction
Machine learning
Microfossils
Morphometry
Proxy
Informática
Computer science
topic Feature extraction
Machine learning
Microfossils
Morphometry
Proxy
Informática
Computer science
description Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/7378
url http://hdl.handle.net/10259/7378
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Microscopy and Microanalysis. 2020, V. 26, n. 6, p. 1158-1167
https://doi.org/10.1017/S1431927620024629
info:eu-repo/grantAgreement/MINECO//TIN2015-67534-P
info:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17
info:eu-repo/grantAgreement/AGAUR//2017 SGR 212
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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