Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

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 in wellness 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

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR 2019, Ottawa, Canada, June 10-13

First Page

399

Last Page

403

ISBN

9781450367653

Identifier

10.1145/3323873.3326923

Publisher

ACM

City or Country

Ottawa

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