Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Christodoulidis, Stergios; Anthimopoulos, Marios; Mougiakakou, Stavroula (21 August 2015). Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks. In: Murino, Vittorio; Puppo, Enrico; Sona, Diego; Cristani, Marco; Sansone, Carlo (eds.) ICIAP 2015 International Workshops. Lecture Notes in Computer Science: Vol. 9281 (pp. 458-465). Springer 10.1007/978-3-319-23222-5_56

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Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Christodoulidis, Stergios; Anthimopoulos, Marios and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISBN:

978-3-319-23221-8

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

04 Mar 2016 07:49

Last Modified:

04 Mar 2016 07:49

Publisher DOI:

10.1007/978-3-319-23222-5_56

URI:

https://boris.unibe.ch/id/eprint/77897

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