demonstration

Magical Rice Bowl: A Real-time Food Category Changer

Authors:

Ryosuke Tanno,

Daichi Horita,

Wataru Shimoda,

Keiji YanaiAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1244 - 1246

https://doi.org/10.1145/3240508.3241391

Published: 15 October 2018 Publication History

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Abstract

In this demo, we demonstrate "Real-time Food Category Change'' based on a Conditional Cycle GAN (cCycle GAN) with a large-scale food image data collected from the Twitter Stream. Conditional Cycle GAN is an extension of CycleGAN, which enables "Food Category Change'' among ten kinds of typical foods served in bowl-type dishes such as beef rice bowl and ramen noodles. The proposed system enables us to change the appearance of a given food photo according to the given category keeping the shape of the given food but exchanging its textures. For training, we used two hundred and thirty thousand food images which achieved very natural food category change among ten kinds of typical Japanese foods: ramen noodle, curry rice, fried rice, beef rice bowl, chilled noodle, spaghetti with meat source, white rice, eel bowl, and fried noodle.

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Cited By

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Yanai KHorita DCho J(2020)Food Image Generation and Translation and Its Application to Augmented Reality2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR49039.2020.00045(181-186)Online publication date: Aug-2020
https://doi.org/10.1109/MIPR49039.2020.00045
Yanai KOkamoto KNagano THorita D(2019)Large-Scale Twitter Food Photo Mining and Its Applications2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM.2019.00-40(77-85)Online publication date: Sep-2019
https://doi.org/10.1109/BigMM.2019.00-40

Index Terms

Magical Rice Bowl: A Real-time Food Category Changer
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia content creation

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Information

Published In

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

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JSPS KAKENHI

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MM '18

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SIGMM

MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Yanai KHorita DCho J(2020)Food Image Generation and Translation and Its Application to Augmented Reality2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR49039.2020.00045(181-186)Online publication date: Aug-2020
https://doi.org/10.1109/MIPR49039.2020.00045
Yanai KOkamoto KNagano THorita D(2019)Large-Scale Twitter Food Photo Mining and Its Applications2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM.2019.00-40(77-85)Online publication date: Sep-2019
https://doi.org/10.1109/BigMM.2019.00-40

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