Fine-Grained Food Classification Methods on the UEC FOOD-100 Database

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dc.authorscopusid57220954985
dc.authorscopusid57220962993
dc.authorscopusid36782998200
dc.authorscopusid24824171900
dc.contributor.authorArslan, B.
dc.contributor.authorMemis, S.
dc.contributor.authorSonmez, E.B.
dc.contributor.authorBatur, O.Z.
dc.date.accessioned2024-07-18T20:17:11Z
dc.date.available2024-07-18T20:17:11Z
dc.date.issued2022
dc.description.abstractThe development of an automatic food recognition system has severalinteresting applications ranging from waste food management, to advertisement, to calorie estimation, and daily diet monitoring. Despite the importance of this subject, the number of related studies is still limited. Moreover, the comparison in the literature was currently done over the best-shot performance without considering the most common method of averaging over several trials. This article surveys the most common deep learning methods used for food classification, it presents the publicly available databases of food, it releases benchmark results for the food classification experiment averaged over five-trials, and it beats the current best-shot performance experiment reaching the state-of-the-art accuracy of 90.02% on the UEC Food-100 database. The best results have been achieved by the ensemble method averaging the predictions of ResNeXt and DenseNet models. All the experiments are run on the UEC Food-100 database because it is one of the most used databases, and it is challenging due to the presence of multifood images, which need to be cropped before processing. This article aims to contribute to automatic food recognition by presenting the most common algorithms used for food classification, introducing the main databases of food items currently available, and reaching the state-of-the-art performance in the best-shot classification experiment of the UEC Food-100 database. That is, this article improves the current best-shot performance by 0.44 percentage points, and fixes it to 90.02%. Furthermore, with the best of our knowledge, this is the first article to introduce to the research community comparison of performances of the classification experiment on the UEC Food-100 database averaged over five-trails. As expected, performance averaged is slightly lower thanthe best-shot one. © 2020 IEEE.en_US
dc.identifier.doi10.1109/TAI.2021.3108126
dc.identifier.endpage243en_US
dc.identifier.issn2691-4581
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85132953119en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage238en_US
dc.identifier.urihttps://doi.org/10.1109/TAI.2021.3108126
dc.identifier.urihttps://hdl.handle.net/11411/6437
dc.identifier.volume3en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Transactions on Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectİmage Classificationen_US
dc.subjectMachine Learningen_US
dc.subjectBenchmarkingen_US
dc.subjectClassification (Of İnformation)en_US
dc.subjectDatabase Systemsen_US
dc.subjectDeep Neural Networksen_US
dc.subjectFeature Extractionen_US
dc.subjectWaste Managementen_US
dc.subjectClassification Algorithmen_US
dc.subjectClassification Methodsen_US
dc.subjectDeep Learningen_US
dc.subjectFeatures Extractionen_US
dc.subjectFine Graineden_US
dc.subjectImages Classificationen_US
dc.subjectMachine-Learningen_US
dc.subjectNeural-Networksen_US
dc.subjectResidual Neural Networken_US
dc.subjectTask Analysisen_US
dc.subjectImage Recognitionen_US
dc.titleFine-Grained Food Classification Methods on the UEC FOOD-100 Databaseen_US
dc.typeArticleen_US

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