research-article

Estimation of the Attractiveness of Food Photography Focusing on Main Ingredients

Authors:

Kazuma Takahashi,

Yasutomo Kawanishi,

Takatsugu Hirayama,

Daisuke Deguchi,

Hiroshi MuraseAuthors Info & Claims

CEA2017: Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

Pages 1 - 6

https://doi.org/10.1145/3106668.3106670

Published: 20 August 2017 Publication History

Abstract

This research aims to develop a method to estimate the attractiveness of a food photo. The proposed method extracts two kinds of image features: 1) those focused on the appearance of the main ingredient, and 2) those focused on the impression of the entire food photo. The former is newly introduced in this paper, whereas the latter is based on previous research. The proposed method integrates these image features with a regression scheme to estimate the attractiveness of an arbitrary food photo. We have also built and released a food image dataset composed of images of ten food categories taken from 36 angles named NU FOOD 360x10. The images were assigned target values of their attractiveness through subjective experiments. Experimental results showed the effectiveness of integrating both kinds of image features.

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Kazuma Takahashi, Keisuke Doman, Yasutomo Kawanishi, Takatsugu Hirayama, Ichiro Ide, Daisuke Deguchi, and Hiroshi Murase. 2016. A study on estimating the attractiveness of food photography. In Proc. 2nd IEEE International Conference on Multimedia Big Data. 444--449.

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Cited By

Morinaga TPatanukhom KSomchit Y(2024)Analyzing the Attractiveness of Food Images Using an Ensemble of Deep Learning Models Trained via Social Media ImagesBig Data and Cognitive Computing10.3390/bdcc80600548:6(54)Online publication date: 27-May-2024
https://doi.org/10.3390/bdcc8060054
Toet AKaneko Dde Kruijf IUshiama Svan Schaik MBrouwer AKallen Vvan Erp J(2019)CROCUFID: A Cross-Cultural Food Image Database for Research on Food Elicited Affective ResponsesFrontiers in Psychology10.3389/fpsyg.2019.0005810Online publication date: 25-Jan-2019
https://doi.org/10.3389/fpsyg.2019.00058
TAKAHASHI KHATTORI TDOMAN KKAWANISHI YHIRAYAMA TIDE IDEGUCHI DMURASE H(2019)Estimation of the Attractiveness of Food Photography Based on Image FeaturesIEICE Transactions on Information and Systems10.1587/transinf.2018EDL8219E102.D:8(1590-1593)Online publication date: 1-Aug-2019
https://doi.org/10.1587/transinf.2018EDL8219
Show More Cited By

Index Terms

Estimation of the Attractiveness of Food Photography Focusing on Main Ingredients
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

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Published In

cover image ACM Other conferences

CEA2017: Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

August 2017

64 pages

ISBN:9781450352673

DOI:10.1145/3106668

General Chair:
Ichiro Ide
Nagoya University
,
Program Chair:
Yoko Yamakata
The University of Tokyo

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

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CEA2017

CEA2017: 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence

August 20, 2017

Melbourne, Australia

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CEA2017 Paper Acceptance Rate 7 of 12 submissions, 58%;

Overall Acceptance Rate 20 of 33 submissions, 61%

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Cited By

Morinaga TPatanukhom KSomchit Y(2024)Analyzing the Attractiveness of Food Images Using an Ensemble of Deep Learning Models Trained via Social Media ImagesBig Data and Cognitive Computing10.3390/bdcc80600548:6(54)Online publication date: 27-May-2024
https://doi.org/10.3390/bdcc8060054
Toet AKaneko Dde Kruijf IUshiama Svan Schaik MBrouwer AKallen Vvan Erp J(2019)CROCUFID: A Cross-Cultural Food Image Database for Research on Food Elicited Affective ResponsesFrontiers in Psychology10.3389/fpsyg.2019.0005810Online publication date: 25-Jan-2019
https://doi.org/10.3389/fpsyg.2019.00058
TAKAHASHI KHATTORI TDOMAN KKAWANISHI YHIRAYAMA TIDE IDEGUCHI DMURASE H(2019)Estimation of the Attractiveness of Food Photography Based on Image FeaturesIEICE Transactions on Information and Systems10.1587/transinf.2018EDL8219E102.D:8(1590-1593)Online publication date: 1-Aug-2019
https://doi.org/10.1587/transinf.2018EDL8219
Hattori TDoman KIde IMekada YIde IYamakata Y(2018)A study on the factors affecting the attractiveness of food photographyProceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management10.1145/3230519.3230592(25-28)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1145/3230519.3230592
Sato AHirayama TDoman KKawanishi YIde IDeguchi DMurase H(2018)Gaze-Inspired Learning for Estimating the Attractiveness of a Food Photo2018 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2018.00015(36-43)Online publication date: Dec-2018
https://doi.org/10.1109/ISM.2018.00015
Yuichiro F(2018)Projection mapping for enhancing the perceived deliciousness of foodIEEE Access10.1109/ACCESS.2018.2875775(1-1)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2875775

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