A Deep Learning based Food Recognition System for Lifelog Images

Binh T. Nguyen, Duc-Tien Dang-Nguyen, Tien X. Dang, Thai Phat, Cathal Gurrin

2018

Abstract

In this paper, we propose a deep learning based system for food recognition from personal life archive images. The system first identifies the eating moments based on multi-modal information, then tries to focus and enhance the food images available in these moments, and finally, exploits GoogleNet as the core of the learning process to recognise the food category of the images. Preliminary results, experimenting on the food recognition module of the proposed system, show that the proposed system achieves 95:97% classification accuracy on the food images taken from the personal life archive from several lifeloggers, which potentially can be extended and applied in broader scenarios and for different types of food categories.

Paper Citation

in Harvard Style

Nguyen B., Dang-Nguyen D., Dang T., Phat T. and Gurrin C. (2018). A Deep Learning based Food Recognition System for Lifelog Images.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: INDEED, ISBN 978-989-758-276-9, pages 657-664. DOI: 10.5220/0006749006570664

in Bibtex Style

@conference{indeed18,
author={Binh T. Nguyen and Duc-Tien Dang-Nguyen and Tien X. Dang and Thai Phat and Cathal Gurrin},
title={A Deep Learning based Food Recognition System for Lifelog Images},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: INDEED,},
year={2018},
pages={657-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006749006570664},
isbn={978-989-758-276-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: INDEED,
TI - A Deep Learning based Food Recognition System for Lifelog Images
SN - 978-989-758-276-9
AU - Nguyen B.
AU - Dang-Nguyen D.
AU - Dang T.
AU - Phat T.
AU - Gurrin C.
PY - 2018
SP - 657
EP - 664
DO - 10.5220/0006749006570664