Automatic food recognition system for middle-eastern cuisines

Basit Öğe Kaydı
dc.contributor.authorQaraqe, Marwa
dc.contributor.authorUsman, Muhammad
dc.contributor.authorAhmad, Kashif
dc.contributor.authorSohail, Amir
dc.contributor.authorBoyaci, Ali
dc.date.accessioned2021-01-25T21:47:57Z
dc.date.available2021-01-25T21:47:57Z
dc.date.issued2020
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThe concerns for a healthier diet are increasing day by day, especially in diabetics wherein the aim of healthier diet can only be achieved by keeping a track of daily food intake and glucose-level. As a consequence, there is an ever-increasing need for automatic tools able to help diabetics to manage their diet and also help physicians to better analyse the effects of various types of food on the glucose-level of diabetics. In this paper, we propose an intelligent food recognition and tracking system for diabetics, which is potentially an essential part of a mobile application that we propose to couple food intake with the blood glucose-level using glucose measuring sensors. For food recognition, we rely on several feature extraction and classification techniques individually and jointly using an early and three different late fusion techniques, namely (i) Particle Swarm Optimisation (PSO), (ii) Genetic Algorithms (GA) based fusion and (iii) simple averaging. Moreover, we also evaluate the performance of several handcrafted and deep features and compare the results against state-of-the-art. In addition, we collect a large-scale dataset containing images from several types of local Middle-Eastern food, which is intended to become a powerful support tool for future research in the domain.en_US
dc.identifier.doi10.1049/iet-ipr.2019.1051en_US
dc.identifier.endpage2479en_US
dc.identifier.issn1751-9659
dc.identifier.issn1751-9667
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85091447419en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2469en_US
dc.identifier.urihttps://doi.org/10.1049/iet-ipr.2019.1051
dc.identifier.urihttps://hdl.handle.net/11467/4459
dc.identifier.volume14en_US
dc.identifier.wosWOS:000571201500019en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherINST ENGINEERING TECHNOLOGY-IETen_US
dc.relation.ispartofIET IMAGE PROCESSINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfeature extractionen_US
dc.subjectgenetic algorithmsen_US
dc.subjectdiseasesen_US
dc.subjectimage fusionen_US
dc.subjectsugaren_US
dc.subjectblooden_US
dc.subjectparticle swarm optimisationen_US
dc.subjectimage classificationen_US
dc.subjectmedical image processingen_US
dc.subjectobject trackingen_US
dc.subjectautomatic toolsen_US
dc.subjectdiabeticsen_US
dc.subjectintelligent food recognitionen_US
dc.subjecttracking systemen_US
dc.subjectmobile applicationen_US
dc.subjectblood glucose levelen_US
dc.subjectglucose measuring sensorsen_US
dc.subjectfeature extractionen_US
dc.subjectclassification techniquesen_US
dc.subjectearly fusion techniquesen_US
dc.subjectgenetic algorithm-based fusionen_US
dc.subjectlocal middle-eastern fooden_US
dc.subjectautomatic food recognition systemen_US
dc.subjectmiddle-eastern cuisinesen_US
dc.subjecthealthier dieten_US
dc.subjectdaily food intakeen_US
dc.subjectparticle swarm optimisationen_US
dc.subjectlarge-scale dataset collectionen_US
dc.titleAutomatic food recognition system for middle-eastern cuisinesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu