A Multi-Task Learning Approach for Meal Assessment

Lu, Ya; Allegra, Dario; Anthimopoulos, Marios; Stanco, Filippo; Farinella, Giovanni Maria; Mougiakakou, Stavroula Georgia (15 July 2018). A Multi-Task Learning Approach for Meal Assessment (In Press). In: 4th International Workshop on Multimedia Assisted Dietary Management.

[img] Text
1806.10343.pdf - Submitted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (522kB) | Request a copy

Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming, expensive and prone to errors. New technology-based methods that provide reliable and convenient dietary assessment, have emerged during the last decade. The advances in the field of computer vision permitted the use of meal image to assess the nutrient content usually through three steps: food segmentation, recognition and volume estimation. In this paper, we propose a use one RGB meal image as input to a multi-task learning based Convolutional Neural Network (CNN). The proposed approach achieved outstanding performance, while a comparison with state-of-the-art methods indicated that the proposed approach exhibits clear advantage in accuracy, along with a massive reduction of processing time.

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 Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Lu, Ya; Anthimopoulos, Marios and Mougiakakou, Stavroula Georgia

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

16 Jul 2018 09:53

Last Modified:

16 Jul 2018 09:53

ArXiv ID:

1806.10343

BORIS DOI:

10.7892/boris.118557

URI:

https://boris.unibe.ch/id/eprint/118557

Actions (login required)

Edit item Edit item
Provide Feedback