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FoodLog: Multimedia Food Recording Tools for Diverse Applications

  • Kiyoharu AizawaEmail author
Chapter

Abstract

Our daily food is an emerging target for multimedia research community. Health care field is paying considerable attention on dietary control, which requires that individuals record what they eat. We developed and made publicly available multimedia applications, that are, FoodLog, multimedia food recording tools that allow users to take photos of their meals and to produce food records. We developed two kinds of tools: One is FoodLog Web and the other is FoodLog app used by smartphones. In both systems, image processing techniques are incorporated. For example, in case of FoodLog app, unlike conventional smartphone-based food recording tools, it allows users to employ meal photos to help them to input textual descriptions based on image retrieval. We summarize the outline of FoodLog, its deployment in diverse applications including health care, and analysis of data captured by a year-long operation of FoodLog app.

Keywords

Food log Food record Dietary assessment Image processing Multimedia 

References

  1. 1.
    F.E. Thompson, A.F. Subar, C.M. Loria, J.L. Reedy, T. Baranowski, Need for technological innovation in dietary assessment. J. Am. Diet. Assoc. 110(1), 4851 (2010)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    FoodLog app. http://app.foodlog.jp
  4. 4.
  5. 5.
    T. Joutou, K. Yanai, in IEEE ICIP. A food image recognition system with multiple kernel learning, pp. 285–288 (2009)Google Scholar
  6. 6.
    F. Zhu, M. Bosch, I. Woo, S.-Y. Kim, C.J. Boushey, D.S. Ebert, E.J. Delp, The use of mobile devices in aiding dietary assessment and evaluation. IEEE J. Sel. Top. Sign. Process. 4(4), 756–766 (2010)CrossRefGoogle Scholar
  7. 7.
    W. Wu, J. Yang, in IEEE ICME, Fast food recognition from videos of eating for calorie estimation, pp. 1210–1213 (2009)Google Scholar
  8. 8.
    S. Yang, M. Chen, D. Pomerleau, R. Sukthankar, in IEEE CVPR. Food recognition using statistics of pairwise local features, pp. 2249–2256 (2010)Google Scholar
  9. 9.
    M. Bosch, F. Zhu, N. Khanna, C.J. Boushey, E. Delp, in IEEE ICIP. Combining global and local features for food identification in dietary assessment, pp. 1789–1792 (2011)Google Scholar
  10. 10.
    Y. Kawano, K. Yanai, FoodCam: a real-time food recognition system on a smartphone. Multimedia Tools Appl. 74(14), 5263–5287 (2015)CrossRefGoogle Scholar
  11. 11.
    L. Bossard, M.Guillaumin, L. Van Gool, Food-101—mining discriminative components with random forests, European Conference on Computer Vision (2014)Google Scholar
  12. 12.
    H. Kagaya, K. Aizawa, M. Ogawa, Food detection and recognition using convolutional neural network. ACM Multimedia 2014, 1085–1088 (2014)Google Scholar
  13. 13.
    K. Kitamura, T. Yamasaki, K. Aizawa, Food log by analyzing food images, ACM Multimedia, pp. 999–1000 (2008)Google Scholar
  14. 14.
    Ministry of Agriculture, Forestry and Fisheries of Japan, Food Balance Guide. http://www.maff.go.jp/j/balance_guide/ (in Japanese)
  15. 15.
    T. Miyazaki, G.C. de Silva, K. Aizawa, in IEEE ISM. Image-based calorie content estimation for dietary assessment, pp. 363–368 (2011)Google Scholar
  16. 16.
    K. Aizawa, Y. Maruyama, H. Li, C. Morikawa, Food balance estimation by using personal dietary tendencies in a multimedia food log. IEEE Trans. Multimedia 15(8), 2176–2185 (2013)CrossRefGoogle Scholar
  17. 17.
    M. Ogawa, Y. Sato, K. Aizawa, Foo.Log.Inc—Counting Calories with Your Camera (2011). www.health2con.com/tv/foo-log-inc-tools-andtrackers
  18. 18.
    K. Aizawa, M. Ogawa et al., Comparative study of the routine daily usability of foodlog: a smartphone-based food recording tool assisted by image retrieval. J. Diabetes Sci. Technol. 8, 203–208 (2014)CrossRefGoogle Scholar
  19. 19.
    J. Noronha, E. Hysen, H. Zhang, K.Z. Gajos, in ACM UIST. PlateMate: crowdsourcing nutrition analysis from food photographs, pp. 1–12 (2011)Google Scholar
  20. 20.
    S. Amano, K. Aizawa, M. Ogawa, Food category representatives: extracting categories from meal names in food recordings and recipe data. IEEE Multimedia Big Data 48–55 (2015)Google Scholar
  21. 21.
    K. Waki, K. Aizawa et al., Dialbetics with a multimedia food recording tool, foodlog: smartphone-based self-management for type 2 diabetes. J. Diabetes Sci. Technol. 9(3), 534–540 (2015)CrossRefGoogle Scholar
  22. 22.
    K. Aizawa, Multimedia foodlog: diverse applications from self-monitoring to social contributions. ITE Trans. Media Technol. Appl. 1(3), 214–219 (2013)CrossRefGoogle Scholar
  23. 23.

Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan

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