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Tastes and Textures Estimation of Foods Based on the Analysis of Its Ingredients List and Image

  • Hiroki Matsunaga
  • Keisuke Doman
  • Takatsugu Hirayama
  • Ichiro IdeEmail author
  • Daisuke Deguchi
  • Hiroshi Murase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Recently, the number of cooking recipes on the Web is increasing. However, it is difficult to search them by tastes or textures although they are actually important considering the nature of the contents. Therefore, we propose a method for estimating the tastes and the textures of a cooking recipe by analyzing them. Concretely, the proposed method refers to an ingredients feature from the “ingredients list” and image features from the “food image” in a cooking recipe. We confirmed the effectiveness of the proposed method through an experiment.

References

  1. 1.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Proc. ECCV 2004 Workshop on Statistical Learning in Computer Vision, pp. 59–74 (May 2004)Google Scholar
  2. 2.
    Dalal, N., Triggs, W.: Histograms of oriented gradients for human detection. In: Proc. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 886–893 (June 2005)Google Scholar
  3. 3.
    Hayakawa, F., Kazami, Y., Nishinari, K., Ioku, K., Akuzawa, S., Yamano, Y., Baba, Y., Kohyama, K.: Classification of Japanese texture terms. J. of Texture Studies 44(2), 140–159 (2013)CrossRefGoogle Scholar
  4. 4.
    Huang, J., Kumar, S.R., Mitra, M., Jing, W., Zabih, Z.: Image indexing using color correlogram. In: Proc. 1997 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 762–768 (June 1997)Google Scholar
  5. 5.
    Kawano, Y., Yanai, K.: Foodcam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications, 1–27 (April 2014)Google Scholar
  6. 6.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proc. 1999 IEEE Int. Conf. on Computer Vision, pp. 1150–1157 (September 1999)Google Scholar
  7. 7.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graphcuts. ACM Trans. on Graphics 23(3), 309–314 (2004)CrossRefGoogle Scholar
  8. 8.
    Tahara, Y., Toko, K.: Electronic tongues –A review. IEEE Sensors J. 13(8), 3001–3011 (2013)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.: The nature of statistical learning theory. Springer, New York (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hiroki Matsunaga
    • 1
  • Keisuke Doman
    • 1
    • 2
  • Takatsugu Hirayama
    • 1
  • Ichiro Ide
    • 1
    Email author
  • Daisuke Deguchi
    • 1
    • 3
  • Hiroshi Murase
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.School of EngineeringChukyo UniversityToyotaJapan
  3. 3.Information Strategy OfficeNagoya UniversityNagoyaJapan

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