Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2018

Abstract

Tracking dietary intake is an important task for health management especially for chronic diseases such as obesity, diabetes, and cardiovascular diseases. Given the popularity of personal hand-held devices, mobile applications provide a promising low-cost solution to tackle the key risk factor by diet monitoring. In this work, we propose a photo based dietary tracking system that employs deep-based image recognition algorithms to recognize food and analyze nutrition. The system is beneficial for patients to manage their dietary and nutrition intake, and for the medical institutions to intervene and treat the chronic diseases. To the best of our knowledge, there are no popular applications in the market that provide a high-performance food photo recognition like ours, which is more convenient and intuitive to enter food than textual typing. We conducted experiments on evaluating the recognition accuracy on laboratory data and real user data on Singapore local food, which shed light on uplifting lab trained image recognition models in real applications. In addition, we have conducted user study to verify that our proposed method has the potential to foster higher user engagement rate as compared to existing apps based dietary tracking approaches.

Keywords

Dietary app, Food image recognition, User food photo

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

PMultiMedia Modeling: 24th International Conference, MMM 2018, Bangkok, February 5-7: Proceedings

Volume

10705

First Page

129

Last Page

141

ISBN

9783319735993

Identifier

10.1007/978-3-319-73600-6_12

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-319-73600-6_12

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