Highly Accurate Food/Non-Food Image Classification Based on a Deep Convolutional Neural Network

  • Hokuto KagayaEmail author
  • Kiyoharu Aizawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


“Food” is an emerging topic of interest for multimedia and computer vision community. In this paper, we investigate food/non-food classification of images. We show that CNN, which is the state of the art technique for general object classification, can perform accurately for this problem. For the experiments, we used three different datasets of images: (1) images we collected from Instagram, (2) Food-101 and Caltech-256 dataset (3) dataset we used in [4]. We investigated the combinations of training and testing using the all three of them. As a result, we achieved high accuracy 96, 95 and 99% in the three datasets respectively.


Food/Non-Food classification Convolutional neural network Deep learning 


  1. 1.
    Kitamura, K., Yamasaki, T., Aizawa, K.: Food log by analyzing food images. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 999–1000. ACM, October, 2008Google Scholar
  2. 2.
    Aizawa, K., Ogawa, M.: FoodLog: Multimedia Tool for Healthcare Applications. IEEE MultiMedia 22(2), 4–9 (2015)CrossRefGoogle Scholar
  3. 3.
    LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural computation 1(4), 541–551 (1989)CrossRefGoogle Scholar
  4. 4.
    Kagaya, H., Aizawa, K., Ogawa, M.: Food detection and recognition using convolutional neural network. In: Proceedings of the ACM International Conference on Multimedia, pp. 1085–1088. ACM, November 2014Google Scholar
  5. 5.
    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
  6. 6.
    He, Y., Xu, C., Khanna, N., Boushey, C.J., Delp, E.J.: Analysis of food images: features and classification. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2744–2748. IEEE, October 2014Google Scholar
  7. 7.
    Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: PFID: pittsburgh fast-food image dataset. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 289–292. IEEE, November 2009Google Scholar
  8. 8.
    Kawano, Y., Yanai, K.: FoodCam-256: a large-scale real-time mobile food recognitionsystem employing high-dimensional features and compression of classifier weights. In: Proceedings of the ACM International Conference on Multimedia, pp. 761–762. ACM, November 2014Google Scholar
  9. 9.
    Lin, M., Chen, Q., Yan, S.: Network in network. In: Proceedings of International Conference on Learning Representations (2014)Google Scholar
  10. 10.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678. ACM, November 2014Google Scholar
  11. 11.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)Google Scholar
  12. 12.
    Bosch, M., Zhu, F., Khanna, N., Boushey, C.J., Delp, E.J.: Combining global and local features for food identification in dietary assessment. In: IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 2011, pp. 1789–1792 (2011). doi: 10.1109/ICIP.2011.6115809
  13. 13.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1717–1724. IEEE, June 2014Google Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012: Neural Information Processing Systems, Lake Tahoe, NevadaGoogle Scholar
  15. 15.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of Interdisciplinary Information StudiesThe University of TokyoTokyoJapan
  2. 2.Department of Information and Communication EngineeringThe University of TokyoTokyoJapan

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