FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

  • Parisa PouladzadehEmail author
  • Abdulsalam Yassine
  • Shervin Shirmohammadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


Food detection, classification, and analysis have been the topic of in-depth studies for a variety of applications related to eating habits and dietary assessment. For the specific topic of calorie measurement of food portions with single and mixed food items, the research community needs a dataset of images for testing and training. In this paper we introduce FooDD: a Food Detection Dataset of 3000 images that offer variety of food photos taken from different cameras with different illuminations. We also provide examples of food detection using graph cut segmentation and deep learning algorithms.


Food image dataset Calorie measurement Food detection 


  1. 1.
    Pouladzadeh, P., Shirmohammadi, S., Almaghrabi, R.: Measuring Calorie and Nutrition from Food Image. IEEE Transactions on Instrumentation & Measurement 63(8), 1947–1956 (2014)CrossRefGoogle Scholar
  2. 2.
    Pouladzadeh, P., Shirmohammadi, S., Bakirov, A., Bulut, A., Yassine, A.: Cloud-Based SVM for Food Categorization. Multimedia Tools and Applications, p. 18. Springer, June 3, 2014. doi:  10.1007/s11042-014-2116-x
  3. 3.
    Pouladzadeh, P., Shirmohammadi, Yassine, A.: Using graph cut segmentation for food calorie measurement. In: IEEE International Symposium on Medical Measurements and Applications, pp. 1–6, June 2014Google Scholar
  4. 4.
    Yuri, Y.B., Lea, G.F.: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., and Hinton, G.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NIPS) (2012)Google Scholar
  6. 6.
    Shirmohammadi, S., Ferrero, A.: Camera as the Instrument: The Rising Trend of Vision Based Measurement. IEEE Instrumentation and Measurement Magazine 17(3), 41–47 (2014)CrossRefGoogle Scholar
  7. 7.
    Sun, M., et al.: Determination of food portion size by image processing. Engineering in Medicine and Biology Society, 871–874, August 2008Google Scholar
  8. 8.
    Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.L.: New support vector algorithms. Neural Computation 12(5), 1207–1245 (2000)CrossRefGoogle Scholar
  9. 9.
    Burke, L.E., et al.: Self-monitoring dietary intake: current andfuture practices. Journal of renal nutrition the official journal of the Council on Renal Nutrition of the National Kidney Foundation 15(3), 281–290 (2005)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Beasley, J.: The pros and cons of using pdas for dietary self-monitoring. J. Am. Diet Assoc. 107(5), 739 (2007)CrossRefGoogle Scholar
  11. 11.
    Gao, C., Kong, F., Tan, J.: Healthaware: tackling obesity with health aware smart phone systems. In: IEEE International Conference on Robotics and Biometics, pp. 1549–1554 (2009)Google Scholar
  12. 12.
    Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: PFID: pittsburgh fast-food image dataset. In: International Conference on Image Processing, pp. 289–292 (2009)Google Scholar
  13. 13.
    Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 446–461. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Kawano, Y., Yanai, K.: Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8927, pp. 3–17. Springer, Heidelberg (2015)Google Scholar
  15. 15.
    He, Y., Xu, C., Khanna, N, Boushey, C.J., Delp, E.J.: Analysis of food images: features and classification. In: IEEE International Conference on Image Processing (ICIP), pp. 2744–2748 (2014)Google Scholar
  16. 16.
    Kong, F., Tan, J.: DietCam: regular shape food recognition with a camera phone. In: International Conference on Body Sensor Networks (BSN), pp. 127–132 (2011)Google Scholar
  17. 17.
    Farinella, G.M., Allegra, D., Stanco, F.: A benchmark dataset to study the representation of food images. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8927, pp. 584–599. Springer, Heidelberg (2015)Google Scholar
  18. 18.
    Kawano, Y., Yanai, K.: FoodCam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications. Springer, April 12, 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Parisa Pouladzadeh
    • 1
    Email author
  • Abdulsalam Yassine
    • 1
  • Shervin Shirmohammadi
    • 1
    • 2
  1. 1.Distributed and Collaborative Virtual Environments Research LaboratoryUniversity of OttawaOttawaCanada
  2. 2.Colleges of Engineering and Natural SciencesIstanbul Şehir UniversityIstanbulTurkey

Personalised recommendations