FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

1. Pouladzadeh, P., Shirmohammadi, S., Almaghrabi, R.: Measuring Calorie and Nutrition from Food Image. IEEE Transactions on Instrumentation & Measurement 63(8), 1947–1956 (2014)

   Article  Google Scholar 

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. 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 2014

   Google Scholar 

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)

   Article  Google Scholar 

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. 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)

   Article  Google Scholar 

7. Sun, M., et al.: Determination of food portion size by image processing. Engineering in Medicine and Biology Society, 871–874, August 2008

   Google Scholar 

8. Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.L.: New support vector algorithms. Neural Computation 12(5), 1207–1245 (2000)

   Article  Google Scholar 

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)

   Article  MathSciNet  Google Scholar 

10. Beasley, J.: The pros and cons of using pdas for dietary self-monitoring. J. Am. Diet Assoc. 107(5), 739 (2007)

    Article  Google Scholar 

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. 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. 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. 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. 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. 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. 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. Kawano, Y., Yanai, K.: FoodCam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications. Springer, April 12, 2014

    Google Scholar 

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