A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Anthimopoulos, Marios M.; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula (2014). A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model. IEEE journal of biomedical and health informatics, 18(4), pp. 1261-2194. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2014.2308928

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Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Item Type:

Journal Article (Original Article)

Division/Institute:

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
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Anthimopoulos, Marios; Diem, Peter and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering

ISSN:

2168-2194

Publisher:

Institute of Electrical and Electronics Engineers

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

06 Oct 2014 16:05

Last Modified:

27 Dec 2016 13:19

Publisher DOI:

10.1109/JBHI.2014.2308928

PubMed ID:

25014934

Uncontrolled Keywords:

Bag of features (BoF), diabetes, feature extraction, food recognition, image classification

BORIS DOI:

10.7892/boris.52864

URI:

https://boris.unibe.ch/id/eprint/52864

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