Advertisement

Learning CNN-based Features for Retrieval of Food Images

  • Gianluigi Ciocca
  • Paolo Napoletano
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10590)

Abstract

Recently a huge amount of work has been done in order to develop Convolutional Neural Networks (CNNs) for supervised food recognition. These CNNs are trained to classify a predefined set of food classes within a specific food dataset. CNN-based features have been largely experimented for many image retrieval domains and to a lesser extent to the food domain. In this paper, we investigate the use of CNN-based features for food retrieval by taking advantage of existing food datasets. To this end, we have built the Food524DB, the largest publicly available food dataset with 524 food classes and 247,636 images by merging food classes from existing datasets in the state of the art. We have then used this dataset to fine tune a Residual Network, ResNet-50, which has demonstrated to be very effective for image recognition. The last fully connected layer is finally used as feature vector for food image indexing and retrieval. Experimental results are reported on the UNICT-FD1200 dataset that has been specifically design for food retrieval.

Keywords

Food retrieval Food dataset Food recognition CNN-based features 

Notes

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

References

  1. 1.
    Akpro Hippocrate, E.A., Suwa, H., Arakawa, Y., Yasumoto, K.: Food weight estimation using smartphone and cutlery. In: Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems, IoT of Health 2016, pp. 9–14. ACM (2016)Google Scholar
  2. 2.
    Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inf. 18(4), 1261–1271 (2014)CrossRefGoogle Scholar
  3. 3.
    Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G., Essa, I.: Leveraging context to support automated food recognition in restaurants. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 580–587 (2015)Google Scholar
  4. 4.
    Bianco, S., Ciocca, G., Napoletano, P., Schettini, R., Margherita, R., Marini, G., Pantaleo, G.: Cooking action recognition with iVAT: an interactive video annotation tool. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 631–641. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41184-7_64 CrossRefGoogle 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. LNCS, vol. 8694, pp. 446–461. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10599-4_29 Google Scholar
  6. 6.
    Chen, J., Ngo, C.W.: Deep-based ingredient recognition for cooking recipe retrieval. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 32–41. ACM (2016)Google 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 (2009)Google Scholar
  8. 8.
    Chen, M.Y., Yang, Y.H., Ho, C.J., Wang, S.H., Liu, S.M., Chang, E., Yeh, C.H., Ouhyoung, M.: Automatic chinese food identification and quantity estimation. In: SIGGRAPH Asia 2012 Technical Briefs, p. 29. ACM (2012)Google Scholar
  9. 9.
    Ciocca, G., Napoletano, P., Schettini, R.: Food recognition and leftover estimation for daily diet monitoring. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 334–341. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23222-5_41 CrossRefGoogle Scholar
  10. 10.
    Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments and results. IEEE J. Biomed. Health Inf. 21(3), 588–598 (2017)CrossRefGoogle Scholar
  11. 11.
    Cusano, C., Napoletano, P., Schettini, R.: Intensity and color descriptors for texture classification. In: IS&T/SPIE Electronic Imaging, p. 866113. International Society for Optics and Photonics (2013)Google Scholar
  12. 12.
    Cusano, C., Napoletano, P., Schettini, R.: Combining local binary patterns and local color contrast for texture classification under varying illumination. JOSA A 31(7), 1453–1461 (2014)CrossRefGoogle Scholar
  13. 13.
    Farinella, G.M., Allegra, D., Moltisanti, M., Stanco, F., Battiato, S.: Retrieval and classification of food images. Comput. Biol. Med. 77, 23–39 (2016)CrossRefGoogle Scholar
  14. 14.
    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. LNCS, vol. 8927, pp. 584–599. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16199-0_41 Google Scholar
  15. 15.
    Hassannejad, H., Matrella, G., Ciampolini, P., De Munari, I., Mordonini, M., Cagnoni, S.: Food image recognition using very deep convolutional networks. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, MADiMa 2016, pp. 41–49. ACM (2016)Google Scholar
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  17. 17.
    He, Y., Xu, C., Khanna, N., Boushey, C., Delp, E.: Analysis of food images: features and classification. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2744–2748 (2014)Google Scholar
  18. 18.
    Hoashi, H., Joutou, T., Yanai, K.: Image recognition of 85 food categories by feature fusion. In: IEEE International Symposium on Multimedia (ISM) 2010, pp. 296–301. IEEE (2010)Google Scholar
  19. 19.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  20. 20.
    Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 285–288. IEEE (2009)Google Scholar
  21. 21.
    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. LNCS, vol. 8927, pp. 3–17. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16199-0_1 Google Scholar
  22. 22.
    Kawano, Y., Yanai, K.: Food image recognition with deep convolutional features. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 Adjunct, pp. 589–593 (2014)Google Scholar
  23. 23.
    Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Ma, Y.: DeepFood: deep learning-based food image recognition for computer-aided dietary assessment. In: Chang, C.K., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 37–48. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39601-9_4 CrossRefGoogle Scholar
  24. 24.
    Mariappan, A., Bosch, M., Zhu, F., Boushey, C.J., Kerr, D.A., Ebert, D.S., Delp, E.J.: Personal dietary assessment using mobile devices, vol. 7246, pp. 72460Z-1–72460Z-12 (2009)Google Scholar
  25. 25.
    Martinel, N., Foresti, G.L., Micheloni, C.: Wide-slice residual networks for food recognition. arXiv preprint arXiv:1612.06543 (2016)
  26. 26.
    Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: 2012 IEEE International Conference on Multimedia and Expo (ICME), pp. 25–30 (2012)Google Scholar
  27. 27.
    Nguyen, D.T., Zong, Z., Ogunbona, P.O., Probst, Y., Li, W.: Food image classification using local appearance and global structural information. Neurocomputing 140, 242–251 (2014)CrossRefGoogle Scholar
  28. 28.
    Pouladzadeh, P., Kuhad, P., Peddi, S.V.B., Yassine, A., Shirmohammadi, S.: Food calorie measurement using deep learning neural network. In: IEEE International Instrumentation and Measurement Technology Conference, pp. 1–6 (2016)Google Scholar
  29. 29.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 806–813 (2014)Google Scholar
  31. 31.
    Wang, X., Kumar, D., Thome, N., Cord, M., Precioso, F.: Recipe recognition with large multimodal food dataset. In: 2015 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6. IEEE (2015)Google Scholar
  32. 32.
    Yanai, K., Kawano, Y.: Food image recognition using deep convolutional network with pre-training and fine-tuning. In: 2015 IEEE International Conference on Multimedia Expo Workshops (ICMEW), pp. 1–6 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
  • Paolo Napoletano
    • 1
  • Raimondo Schettini
    • 1
  1. 1.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)Università degli Studi di Milano-BicoccaMilanoItaly

Personalised recommendations