Abstract
This paper considers the problem of simultaneous restaurant and dish recognition from food images. Since the restaurants are known because of their some special dishes (e.g., the dish “hamburger” in the restaurant “KFC” ), the dish semantics from the food image provides partial evidence for the restaurant identity. Therefore, instead of exploiting the binary correlation between food images and dish labels by existing work, we model food images, their dish names and restaurant information jointly, which is expected to enable novel applications, such as food image based restaurant visualization and recommendation. For solution, we propose a model, namely Partially Asymmetric Multi-Task Convolutional Neural Network (PAMT-CNN), which includes the dish pathway and the restaurant pathway to learn the dish semantics and the restaurant identity, respectively. Considering the dependence of the restaurant identity on the dish semantics, PAMT-CNN is capable of learning the restaurant’s identity under the guidance of the dish pathway using partially asymmetric shared network architecture. To evaluate our model, we construct one food image dataset with 24,690 food images, 100 classes of restaurants and 100 classes of dishes. The evaluation results on this dataset have validated the effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
The largest venue review website in China, similar to Yelp.
References
Beijbom, O., Joshi, N., Morris, D., Saponas, S., Khullar, S.: Menu-match: restaurant-specific food logging from images. In: Applications of Computer Vision, pp. 844–851 (2015)
Bengio, Y.: Learning deep architectures for ai. Mach. Learn. 2(1), 1–127 (2009)
Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G.D., Essa, I.: Leveraging context to support automated food recognition in restaurants. In: Applications of Computer Vision, pp. 580–587 (2015)
Bossard, L., Guillaumin, M., 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, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_29
Cordeiro, F., Bales, E., Cherry, E., Fogarty, J.: Rethinking the mobile food journal: exploring opportunities for lightweight photo-based capture. In: Proceedings ACM, pp. 3207–3216 (2015)
Ge, M., Ricci, F., Massimo, D.: Health-aware food recommender system. In: ACM, pp. 333–334 (2015)
Herranz, L., Xu, R., Jiang, S.: A probabilistic model for food image recognition in restaurants. In: ICME (2015)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: ACM MM, pp. 675–678 (2014)
Kadowaki, T., Yamakata, Y., Tanaka, K.: Situation-based food recommendation for yielding good results. In: ICMEW, pp. 1–6 (2015)
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 ACM MM, pp. 761–762 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in NIPS, pp. 1097–1105 (2012)
Liu, S., Cui, P., Zhu, W., Yang, S.: Learning socially embedded visual representation from scratch. In: Proceedings of ACM MM, pp. 109–118 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_53
Meyers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., Murphy, K.P.: Im2calories: towards an automated mobile vision food diary. In: Proceedings of ICCV, pp. 1233–1241 (2015)
Wang, S., Jiang, S.: Instre: a new benchmark for instance-level object retrieval and recognition. ACM Trans. Multimedia Comput. Commun. Appl. 11(3), 1–21 (2015)
Xu, R., Herranz, L., Jiang, S., Wang, S., Song, X., Jain, R.: Geolocalized modeling for dish recognition. IEEE TMM 17(8), 1187–1199 (2015)
Yang, L., Cui, Y., Zhang, F., Pollak, J.P., Belongie, S., Estrin, D.: Plateclick: bootstrapping food preferences through an adaptive visual interface. In: Proceedings of KSEM, pp. 183–192 (2015)
Yang, S., Chen, M., Pomerleau, D., Sukthankar, R.: Food recognition using statistics of pairwise local features. In: CVPR, pp. 2249–2256 (2010)
Acknowledgements
This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2012CB316400, the National Natural Science Foundation of China under Grant Nos. 61532018 and 61322212, the National High Technology Research and Development 863 Program of China under Grant No. 2014AA015202, China Postdoctoral Science Foundation under Grant No. 2016M590135, Beijing Science And Technology Project under Grant No. D161100001816001. This work is also funded by Lenovo Outstanding Young Scientists Program (LOYS).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, H., Min, W., Li, X., Jiang, S. (2016). Where and What to Eat: Simultaneous Restaurant and Dish Recognition from Food Image. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-48890-5_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48889-9
Online ISBN: 978-3-319-48890-5
eBook Packages: Computer ScienceComputer Science (R0)