skip to main content
10.1145/3437120.3437265acmotherconferencesArticle/Chapter ViewAbstractPublication PagespciConference Proceedingsconference-collections
research-article

Deep Learning Platforms in Food Recognition

Authors Info & Claims
Published:04 March 2021Publication History

ABSTRACT

Image content prediction with novel deep learning approaches is a hot research topic for many scientific disciplines. Image-based food types recognition and their ingredients is a particularly challenging task, since food dishes are typically deformable objects, usually including complex semantics, which makes the task of defining their structure very difficult. In this paper we introduce a novel web-based system that exploits commercial deep learning based platforms to predict image content for food recognition. In addition to that, the system combines the individual predictions of the platforms to produce more accurate results. Also, we provide a short assessment of the chosen platforms to determine the most efficient one, in the domain of food recognition. The paper is concluded by highlighting the key features of platforms and the advantages of the new presented system.

References

  1. Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014. Food-101 – Mining Discriminative Components with Random Forests. In Computer Vision – ECCV 2014, David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.). Springer International Publishing, Cham, 446–461.Google ScholarGoogle ScholarCross RefCross Ref
  2. Kiourt Chairi, Pavlidis George, and Stella Markantonatou. 2020. Deep learning approaches in food recognition,. In MACHINE LEARNING PARADIGMS - Advances in Theory and Applications of Deep Learning, Tsihrintzis George, A. and Jain Lakhmi (Eds.). Springer, Cham, 83–108. https://doi.org/10.1007/978-3-030-49724-8_4Google ScholarGoogle Scholar
  3. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. ImageNet Classification with Deep Convolutional Neural Networks. 60, 6 (2017). https://doi.org/10.1145/3065386Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Simon Mezgec and Barbara Korousic Seljak. 2017. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment. Nutrients 9, 7 (2017). https://doi.org/10.3390/nu9070657Google ScholarGoogle Scholar
  5. Weiqing Min, Shuqiang Jiang, Linhu Liu, Yong Rui, and Ramesh Jain. 2019. A Survey on Food Computing. ACM Comput. Surv. 52, 5, Article 92 (Sept. 2019), 36 pages. https://doi.org/10.1145/3329168Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. J. Pan and Q. Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 10(2010), 1345–1359.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kawano Y. and Yanai K.2014. Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. In ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV). 3–17.Google ScholarGoogle Scholar
  8. LeCun Y., Bengio Y., and Hinton G.2015. Deep learning. Nature 521(2015), 436–444. https://doi.org/10.1038/nature14539Google ScholarGoogle ScholarCross RefCross Ref
  9. Matsuda Y., Hoashi H., and Yanai K.2012. Recognition of Multiple-Food Images by Detecting Candidate Regions. In IEEE International Conference on Multimedia and Expo (ICME). 25–30. https://doi.org/10.1109/ICME.2012.157Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. K. Yanai and Y. Kawano. 2015. Food image recognition using deep convolutional network with pre-training and fine-tuning. In 2015 IEEE International Conference on Multimedia Expo Workshops (ICMEW). 1–6.Google ScholarGoogle Scholar
  11. Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu, and Yong He. 2019. Application of Deep Learning in Food: A Review. Comprehensive Reviews in Food Science and Food Safety 18, 6(2019), 1793–1811. https://doi.org/10.1111/1541-4337.12492 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12492Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics
    November 2020
    433 pages

    Copyright © 2020 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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 4 March 2021

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate190of390submissions,49%
  • Article Metrics

    • Downloads (Last 12 months)23
    • Downloads (Last 6 weeks)9

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format