Food Recognition Based on Image Retrieval with Global Feature Descriptor

  • Wei SunEmail author
  • Xiaofeng Ji
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


This paper proposes a simple and effective non-parametric approach to solve the problem of food images parsing and label images with their categories. Firstly, the proposed approach works by six types of global image features: CEDD, FCTH, BTDH, EHD, CLD and SCD to matching with global image descriptors, labeling image with their categories, and the distance for each descriptor are fused to get the likelihood probability of each class, then efficient Markov random field (MRF) optimization is proposed for incorporating neighborhood context, besides optimization minimization are used Iterated Conditional Modes (ICM) algorithms. And this paper also introduces a non-parametric, data-driven approaches framework. This approach requires no training, just prior distribution and joint distribution are taken into account, and it can easily scale to data sets with tens of thousands of images and hundreds of labels. At last, the experiments show that the proposed method is significantly more accurate and faster at identifying food than existing methods.


Automatic food recognition Global image descriptors Markov random field 



This work was supported by National Nature Science Foundation of China (NSFC) under Grants 61671356, 61201290.


  1. 1.
    Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: PFID: pittsburgh fast-food image dataset. In: ICIP, pp. 289–292 (2009)Google Scholar
  2. 2.
    Chatzichristofis, S.A., Zagoris, K., Boutalis, Y.S., Papamarkos, N.: Accurate image retrieval based on compact composite descriptors and relevance feedback information. Int. J. Pattern Recognit Artif Intell. 24(02), 207–244 (2010)CrossRefGoogle Scholar
  3. 3.
    Jiang, T., Jurie, F., Schmid, C.: Learning shape prior models for object matching. In: CV PR, pp. 845–855 (2009)Google Scholar
  4. 4.
    Lazebnik, S., Cordelia, S., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)Google Scholar
  5. 5.
    Lin, M.T., Haksar, A., Peron, F.G.: Beyond local appearance: category recognition from pairwise interactions of simple features. In: CVPR (2007)Google Scholar
  6. 6.
    Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: Multimedia and Expo (ICME), pp. 25–30 (2012)Google Scholar
  7. 7.
    Duan, P., Wang, W., Zhang, W., Gong, F., Zhang, P., Rao, Y.: Food Image recognition using pervasive cloud computing. In: Green Computing and Communications (GreenCom), pp. 1631–1637 (2013)Google Scholar
  8. 8.
    Kusumoto, R., Han, X. H., Chen, Y. W.: Sparse model in hierarchic spatial structure for food image recognition. In: BMEI, pp. 851–855 (2013)Google Scholar
  9. 9.
    Kawano, Y., Yanai, K.: Real-time mobile food recognition system. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–7 (2013)Google Scholar
  10. 10.
    Oliveira, L., Costa, V., Neves, G., Oliveira, T., Jorge, E., Lizarraga, M.: A mobile, lightweight, poll-based food identification system. Pattern Recognit. 47(5), 1941–1952 (2014)CrossRefGoogle Scholar
  11. 11.
    Shroff, G., Smailagic, A., Siewiorek, D. P.: Wearable context-aware food recognition for calorie monitoring. In: Wearable Computers (ISWC), pp. 119–120 (2008)Google Scholar
  12. 12.
    Fischer, W.J., Fischer, W.J.: Food intake recognition conception for wearable devices. In: ACM MOBIHOC Workshop on Pervasive Wireless Healthcare, pp. 7. ACM (2011)Google Scholar
  13. 13.
    Yüksel, B.: Automatic food recognition and automatic cooking termination by texture analysis method in camera mounted oven. In: Signal Processing and Communications Applications Conference (SIU), pp. 1987–1990 (2014)Google Scholar
  14. 14.
    Wu, W., Yang, J.: Fast food recognition from videos of eating for calorie estimation. In: Multimedia and Expo (ICME), pp. 1210–1213 (2009)Google Scholar
  15. 15.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)CrossRefGoogle Scholar
  16. 16.
    Lux, M., Chatzichristofis, S. A.: Lire: lucene image retrieval - an extensible java CBIR library. In: ACM International Conference on Multimedia, pp. 1085–1087 (2008)Google Scholar
  17. 17.
    Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: Color and edge directivity descriptor a compact descriptor for image indexing and retrieval. In: 6th International Conference on Computer Vision Systems ICVS, pp. 312–322(2008)Google Scholar
  18. 18.
    Chatzichristofis, S. A., Boutalis, Y. S.: Fcth: fuzzy color and texture histogram - a low level feature for accurate image retrieval. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 191–196 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.School of Aerospace Science and TechnologyXidian UniversityXi’anChina

Personalised recommendations