publication . Preprint . 2018

Food recognition and recipe analysis: integrating visual content, context and external knowledge

Herranz, Luis; Min, Weiqing; Jiang, Shuqiang;
Open Access English
  • Published: 22 Jan 2018
Abstract
Comment: Survey about contextual food recognition and multimodal recipe analysis
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Download from
18 references, page 1 of 2

[1] W. Min, B. K. Bao, S. Mei, Y. Zhu, Y. Rui, and S. Jiang, “You are what you eat: Exploring rich recipe information for cross-region food analysis,” IEEE Transactions on Multimedia, vol. PP, no. 99, p. 1, 2017.

[2] R. Xu, L. Herranz, S. Jiang, S. Wang, X. Song, and R. Jain, “Geolocalized modeling for dish recognition,” IEEE Transactions on Multimedia, vol. 17, no. 8, pp. 1187-1199, 2015.

[3] J. Chen and C.-W. Ngo, “Deep-based ingredient recognition for cooking recipe retrieval,” in Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016, pp. 32-41.

[4] W. Min, S. Jiang, J. Sang, H. Wang, X. Liu, and L. Herranz, “Being a supercook: Joint food attributes and multimodal content modeling for recipe retrieval and exploration,” IEEE Transactions on Multimedia, vol. 19, no. 5, pp. 1100-1113, 2017.

[5] A. Salvador, N. Hynes, Y. Aytar, J. Marin, F. Ofli, I. Weber, and A. Torralba, “Learning cross-modal embeddings for cooking recipes and food images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[6] H. Kagaya, K. Aizawa, and M. Ogawa, “Food detection and recognition using convolutional neural network,” in Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014, pp. 1085-1088. [OpenAIRE]

[7] J. Chen, C.-W. Ngo, and T.-S. Chua, “Cross-modal recipe retrieval with rich food attributes,” in Proceedings of the 2017 ACM on Multimedia Conference, 2017, pp. 1771-1779.

[8] L. Yang, C.-K. Hsieh, H. Yang, J. P. Pollak, N. Dell, S. Belongie, C. Cole, and D. Estrin, “Yum-me: a personalized nutrient-based meal recommender system,” ACM Transactions on Information Systems (TOIS), vol. 36, no. 1, p. 7, 2017. [OpenAIRE]

[9] J. Malmaud, J. Huang, V. Rathod, N. Johnston, A. Rabinovich, and K. Murphy, “What's cookin'? interpreting cooking videos using text, speech and vision,” arXiv preprint arXiv:1503.01558, 2015. [OpenAIRE]

[10] W. Min, S. Jiang, S. Wang, J. Sang, and S. Mei, “A delicious recipe analysis framework for exploring multi-modal recipes with various attributes,” in ACM International Conference on Multimedia, 2017.

[11] L. Herranz, S. Jiang, and R. Xu, “Modeling restaurant context for food recognition,” IEEE Transactions on Multimedia, vol. 19, no. 2, pp. 430- 440, February 2017.

[12] F. Zhou and Y. Lin, “Fine-grained image classification by exploring bipartite-graph labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1124-1133.

[13] J. Chen, L. Pang, and C.-W. Ngo, “Cross-modal recipe retrieval: How to cook this dish?” in International Conference on Multimedia Modeling. Springer, 2017, pp. 588-600.

[14] O. Beijbom, N. Joshi, D. Morris, S. Saponas, and S. Khullar, “Menumatch: Restaurant-specific food logging from images,” in IEEE Winter Conference on Applications of Computer Vision, Jan. 2015, pp. 844-851.

[15] Y. Kawano and K. Yanai, “Foodcam: A real-time mobile food recognition system employing fisher vector,” in International Conference on Multimedia Modeling, 2014, pp. 369-373.

18 references, page 1 of 2
Abstract
Comment: Survey about contextual food recognition and multimodal recipe analysis
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Download from
18 references, page 1 of 2

[1] W. Min, B. K. Bao, S. Mei, Y. Zhu, Y. Rui, and S. Jiang, “You are what you eat: Exploring rich recipe information for cross-region food analysis,” IEEE Transactions on Multimedia, vol. PP, no. 99, p. 1, 2017.

[2] R. Xu, L. Herranz, S. Jiang, S. Wang, X. Song, and R. Jain, “Geolocalized modeling for dish recognition,” IEEE Transactions on Multimedia, vol. 17, no. 8, pp. 1187-1199, 2015.

[3] J. Chen and C.-W. Ngo, “Deep-based ingredient recognition for cooking recipe retrieval,” in Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016, pp. 32-41.

[4] W. Min, S. Jiang, J. Sang, H. Wang, X. Liu, and L. Herranz, “Being a supercook: Joint food attributes and multimodal content modeling for recipe retrieval and exploration,” IEEE Transactions on Multimedia, vol. 19, no. 5, pp. 1100-1113, 2017.

[5] A. Salvador, N. Hynes, Y. Aytar, J. Marin, F. Ofli, I. Weber, and A. Torralba, “Learning cross-modal embeddings for cooking recipes and food images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[6] H. Kagaya, K. Aizawa, and M. Ogawa, “Food detection and recognition using convolutional neural network,” in Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014, pp. 1085-1088. [OpenAIRE]

[7] J. Chen, C.-W. Ngo, and T.-S. Chua, “Cross-modal recipe retrieval with rich food attributes,” in Proceedings of the 2017 ACM on Multimedia Conference, 2017, pp. 1771-1779.

[8] L. Yang, C.-K. Hsieh, H. Yang, J. P. Pollak, N. Dell, S. Belongie, C. Cole, and D. Estrin, “Yum-me: a personalized nutrient-based meal recommender system,” ACM Transactions on Information Systems (TOIS), vol. 36, no. 1, p. 7, 2017. [OpenAIRE]

[9] J. Malmaud, J. Huang, V. Rathod, N. Johnston, A. Rabinovich, and K. Murphy, “What's cookin'? interpreting cooking videos using text, speech and vision,” arXiv preprint arXiv:1503.01558, 2015. [OpenAIRE]

[10] W. Min, S. Jiang, S. Wang, J. Sang, and S. Mei, “A delicious recipe analysis framework for exploring multi-modal recipes with various attributes,” in ACM International Conference on Multimedia, 2017.

[11] L. Herranz, S. Jiang, and R. Xu, “Modeling restaurant context for food recognition,” IEEE Transactions on Multimedia, vol. 19, no. 2, pp. 430- 440, February 2017.

[12] F. Zhou and Y. Lin, “Fine-grained image classification by exploring bipartite-graph labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1124-1133.

[13] J. Chen, L. Pang, and C.-W. Ngo, “Cross-modal recipe retrieval: How to cook this dish?” in International Conference on Multimedia Modeling. Springer, 2017, pp. 588-600.

[14] O. Beijbom, N. Joshi, D. Morris, S. Saponas, and S. Khullar, “Menumatch: Restaurant-specific food logging from images,” in IEEE Winter Conference on Applications of Computer Vision, Jan. 2015, pp. 844-851.

[15] Y. Kawano and K. Yanai, “Foodcam: A real-time mobile food recognition system employing fisher vector,” in International Conference on Multimedia Modeling, 2014, pp. 369-373.

18 references, page 1 of 2
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