Benchmarking algorithms for food localization and semantic segmentation

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dc.contributor.authorAslan, Sinem
dc.contributor.authorCiocca, Gianluigi
dc.contributor.authorMazzini, Davide
dc.contributor.authorSchettini, Raimondo
dc.date.accessioned2020-12-01T11:59:19Z
dc.date.available2020-12-01T11:59:19Z
dc.date.issued2020
dc.departmentEge Üniversitesien_US
dc.description.abstractThe problem of food segmentation is quite challenging since food is characterized by intrinsic high intra-class variability. Also, segmentation of food images taken in-the-wild may be characterized by acquisition artifacts, and that could be problematic for the segmentation algorithms. A proper evaluating of segmentation algorithms is of paramount importance for the design and improvement of food analysis systems that can work in less-than-ideal real scenarios. in this paper, we evaluate the performance of different deep learning-based segmentation algorithms in the context of food. Due to the lack of large-scale food segmentation datasets, we initially create a new dataset composed of 5000 images of 50 diverse food categories. the images are accurately annotated with pixel-wise annotations. in order to test the algorithms under different conditions, the dataset is augmented with the same images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur. the final dataset is composed of 120,000 images. Using standard benchmark measures, we conducted extensive experiments to evaluate ten state-of-the-art segmentation algorithms on two tasks: food localization and semantic food segmentation.en_US
dc.description.sponsorshipNVIDIA Corporation; project FooDesArt: Food Design Arte -L'Arte del Benessere, CUP (Codice Unico Progetto) [E48I16000350009]; POR FESR 2014-2020 (Programma Operativo Regionale, Fondo Europeo di Sviluppo Regionale -Regional Operational Programme, European Regional Development Fund)en_US
dc.description.sponsorshipWe gratefully acknowledge the support of NVIDIA Corporation with the donation of the K40, Titan Xp, and Titan X GPU cards used for this research. This work is published in the context of the project FooDesArt: Food Design Arte -L'Arte del Benessere, CUP (Codice Unico Progetto -Unique Project Code): E48I16000350009 -Call "Smart Fashion and Design", cofunded by POR FESR 2014-2020 (Programma Operativo Regionale, Fondo Europeo di Sviluppo Regionale -Regional Operational Programme, European Regional Development Fund).en_US
dc.identifier.doi10.1007/s13042-020-01153-z
dc.identifier.endpage2847en_US
dc.identifier.issn1868-8071
dc.identifier.issn1868-808X
dc.identifier.issn1868-8071en_US
dc.identifier.issn1868-808Xen_US
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85086879236en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2827en_US
dc.identifier.urihttps://doi.org/10.1007/s13042-020-01153-z
dc.identifier.urihttps://hdl.handle.net/11454/62229
dc.identifier.volume11en_US
dc.identifier.wosWOS:000543043400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofInternational Journal of Machine Learning and Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBenchmarkingen_US
dc.subjectConvolutional neural networken_US
dc.subjectFood localizationen_US
dc.subjectFood segmentationen_US
dc.titleBenchmarking algorithms for food localization and semantic segmentationen_US
dc.typeArticleen_US

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