Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models

In this paper, we propose a method for automatically recognizing different date varieties. The presence of outlier samples could significantly degrade the recognition outcomes. Therefore, we separately prune samples of each variety from outliers using the Pruning Local Distance-based Outlier Factor...

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Detalhes bibliográficos
Autores: Aiadi, Oussama, Kherfi, Mohammed Lamine, Khaldi, Belal
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:206875
Acesso em linha:https://ddd.uab.cat/record/206875
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1041
Access Level:acceso abierto
Palavra-chave:Date fruit
Date recognition
Gaussian mixture model
Outlier detection
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spelling Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture ModelsAiadi, OussamaKherfi, Mohammed LamineKhaldi, BelalDate fruitDate recognitionGaussian mixture modelOutlier detectionIn this paper, we propose a method for automatically recognizing different date varieties. The presence of outlier samples could significantly degrade the recognition outcomes. Therefore, we separately prune samples of each variety from outliers using the Pruning Local Distance-based Outlier Factor (PLDOF) method. Samples of the same variety could have several visual appearances because of the noticeable variation in terms of their visual characteristics. Thus, in order to take this intra-variation into account, we model each variety with a Gaussian Mixture Model (GMM), where each component within the GMM corresponds to one visual appearance. Expectation-Maximization (EM) algorithm was used for parameters estimation and Davies-Bouldin index was used to automatically and precisely estimate the number of components (i.e., appearances). Compared to the related studies, the proposed method 1) is capable to recognize samples though the noticeable variation, in terms of maturity stage and hardness degree, within some varieties; 2) achieves a high recognition rate in spite of the presence of outlier samples; 3) is capable to distinguish between the highly confusing varieties; 4) is fully automatic, as it does not require neither physical measurements nor human assistance. For testing purposes, we introduce a new benchmark which includes the highest number of varieties (11) compared to the previous studies. Experiments show that our method has significantly outperformed several methods, where a high recognition rate of 97.8% has been reached. 22019-01-0120192019-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/206875https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1041reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2068752026-06-06T12:50:31Z
dc.title.none.fl_str_mv Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
title Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
spellingShingle Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
Aiadi, Oussama
Date fruit
Date recognition
Gaussian mixture model
Outlier detection
title_short Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
title_full Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
title_fullStr Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
title_full_unstemmed Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
title_sort Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
dc.creator.none.fl_str_mv Aiadi, Oussama
Kherfi, Mohammed Lamine
Khaldi, Belal
author Aiadi, Oussama
author_facet Aiadi, Oussama
Kherfi, Mohammed Lamine
Khaldi, Belal
author_role author
author2 Kherfi, Mohammed Lamine
Khaldi, Belal
author2_role author
author
dc.subject.none.fl_str_mv Date fruit
Date recognition
Gaussian mixture model
Outlier detection
topic Date fruit
Date recognition
Gaussian mixture model
Outlier detection
description In this paper, we propose a method for automatically recognizing different date varieties. The presence of outlier samples could significantly degrade the recognition outcomes. Therefore, we separately prune samples of each variety from outliers using the Pruning Local Distance-based Outlier Factor (PLDOF) method. Samples of the same variety could have several visual appearances because of the noticeable variation in terms of their visual characteristics. Thus, in order to take this intra-variation into account, we model each variety with a Gaussian Mixture Model (GMM), where each component within the GMM corresponds to one visual appearance. Expectation-Maximization (EM) algorithm was used for parameters estimation and Davies-Bouldin index was used to automatically and precisely estimate the number of components (i.e., appearances). Compared to the related studies, the proposed method 1) is capable to recognize samples though the noticeable variation, in terms of maturity stage and hardness degree, within some varieties; 2) achieves a high recognition rate in spite of the presence of outlier samples; 3) is capable to distinguish between the highly confusing varieties; 4) is fully automatic, as it does not require neither physical measurements nor human assistance. For testing purposes, we introduce a new benchmark which includes the highest number of varieties (11) compared to the previous studies. Experiments show that our method has significantly outperformed several methods, where a high recognition rate of 97.8% has been reached.
publishDate 2019
dc.date.none.fl_str_mv 2
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/206875
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1041
url https://ddd.uab.cat/record/206875
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1041
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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