Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157235 https://doi.org/10.1080/19476337.2023.2263513 |
Resumo: | This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification. |
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Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends.Image classificationImage analysisPrincipal component analysisThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification.DAVY WILLIAM HIDALGO CHÁVEZ, UFRRJ; FELIPE LEITE COELHO DA SILVA, UFRRJ; RENAN VICENTE PINTO, UFRRJ; CARLOS WANDERLEI PILER DE CARVALHO, CTAA; OTNIEL FREITAS SILVA, CTAA.HIDALGO CHÁVEZ, D. W.SILVA, F. L. C. DAPINTO, R. V.CARVALHO, C. W. P. deFREITAS-SILVA, O.2023-10-16T19:25:03Z2023-10-16T19:25:03Z2023-10-162023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCyTA: Journal of Food, v. 21, n. 1, p. 606-613, 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157235https://doi.org/10.1080/19476337.2023.2263513enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-10-16T19:25:04Zoai:www.alice.cnptia.embrapa.br:doc/1157235Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-10-16T19:25:04falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-10-16T19:25:04Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
title |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
spellingShingle |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. HIDALGO CHÁVEZ, D. W. Image classification Image analysis Principal component analysis |
title_short |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
title_full |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
title_fullStr |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
title_full_unstemmed |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
title_sort |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
author |
HIDALGO CHÁVEZ, D. W. |
author_facet |
HIDALGO CHÁVEZ, D. W. SILVA, F. L. C. DA PINTO, R. V. CARVALHO, C. W. P. de FREITAS-SILVA, O. |
author_role |
author |
author2 |
SILVA, F. L. C. DA PINTO, R. V. CARVALHO, C. W. P. de FREITAS-SILVA, O. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
DAVY WILLIAM HIDALGO CHÁVEZ, UFRRJ; FELIPE LEITE COELHO DA SILVA, UFRRJ; RENAN VICENTE PINTO, UFRRJ; CARLOS WANDERLEI PILER DE CARVALHO, CTAA; OTNIEL FREITAS SILVA, CTAA. |
dc.contributor.author.fl_str_mv |
HIDALGO CHÁVEZ, D. W. SILVA, F. L. C. DA PINTO, R. V. CARVALHO, C. W. P. de FREITAS-SILVA, O. |
dc.subject.por.fl_str_mv |
Image classification Image analysis Principal component analysis |
topic |
Image classification Image analysis Principal component analysis |
description |
This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-16T19:25:03Z 2023-10-16T19:25:03Z 2023-10-16 2023 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
CyTA: Journal of Food, v. 21, n. 1, p. 606-613, 2023. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157235 https://doi.org/10.1080/19476337.2023.2263513 |
identifier_str_mv |
CyTA: Journal of Food, v. 21, n. 1, p. 606-613, 2023. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157235 https://doi.org/10.1080/19476337.2023.2263513 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
_version_ |
1794503550587371520 |