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FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector

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MultiMedia Modeling (MMM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8326))

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Abstract

In the demo, we demonstrate a mobile food recognition system with Fisher Vector and liner one-vs-rest SVMs which enables us to record our food habits easily. In the experiments with 100 kinds of food categories, we have achieved the 79.2% classification rate for the top 5 category candidates when the ground-truth bounding boxes are given. The prototype system is open to the public as an Android-based smartphone application.

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References

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Author information

Authors and Affiliations

  1. Department of Informatics, The University of Electro-Communications, Chofu, Tokyo, 182-8585, Japan

    Yoshiyuki Kawano & Keiji Yanai

Authors
  1. Yoshiyuki Kawano
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  2. Keiji Yanai
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Editor information

Editors and Affiliations

  1. School of Computing, Dublin City University, 9, Dublin, Ireland

    Cathal Gurrin

  2. Fakultät IV für Elektrotechnik und Informatik, Technische Universität Berlin / DAI-Labor, 10587, Berlin, Germany

    Frank Hopfgartner

  3. Department of Information and Computing Sciences, Universiteit Utrecht, 3584, Utrecht, CC, The Netherlands

    Wolfgang Hurst

  4. UiT The Arctic University of Norway, 9019, Tromsø, Norway

    HÃ¥vard Johansen

  5. Singapore University of Technology and Design, Singapore

    Hyowon Lee

  6. School of Electrical Engineering, Dublin City University, Ireland

    Noel O’Connor

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© 2014 Springer International Publishing Switzerland

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Kawano, Y., Yanai, K. (2014). FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-04117-9_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04116-2

  • Online ISBN: 978-3-319-04117-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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