@inproceedings{ee1d3001435c46ea9d90c717bdf8a1a6,
title = "DietLens-Eout: Large Scale Restaurant Food Photo Recognition",
abstract = "Restaurant dishes represent a significant portion of food that people consume in their daily life. While people are becoming healthconscious in their food intake, convenient restaurant food tracking becomes an essential task inwellness and fitness applications. Given the huge number of dishes (food categories) involved, it becomes extremely challenging for traditional food photo classification to be feasible in both algorithm design and training data availability. In this work, we present a demo that runs on restaurant dish images in a city of millions of residents and tens of thousand restaurants. We propose a rank-loss based convolutional neural network to optimize the image features representation. Context information such as GPS location of the recognition request is also used to further improve the performance. Our experimental results are highly promising. We have shown in our demo that the proposed algorithm is near ready to be deployed in real-world applications.",
keywords = "Food recognition, Restaurant food recognition",
author = "Zhipeng Wei and Jingjing Chen and Zhaoyan Ming and Chong-Wah Ngo and Tat-Seng Chua and Fengfeng Zhou",
note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 2019 ACM International Conference on Multimedia Retrieval (ICMR'19), ACM ICMR 2019 ; Conference date: 10-06-2019 Through 13-06-2019",
year = "2019",
month = jun,
doi = "10.1145/3323873.3326923",
language = "English",
isbn = "9781450367653",
series = "ICMR - Proceedings of the ACM International Conference on Multimedia Retrieval",
publisher = "Association for Computing Machinery",
pages = "399--403",
booktitle = "ICMR'19",
address = "United States",
}