Mixed Dish Recognition through Multi-Label Learning-Reference-Cited by-同舟云学术

Mixed Dish Recognition through Multi-Label Learning

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 11th Workshop on Multimedia for Cooking and Eating Activities - CEA '19
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Author:

Wang Yunan¹,Chen Jing-jing²,Ngo Chong-Wah³,Chua Tat-Seng²,Zuo Wanli¹,Ming Zhaoyan²

Affiliation:

1. Jilin University, Changchun, China

2. National University of Singapore, Singapore, Singapore

3. City University of Hong Kong, Hong Kong, China

Funder

Nature Science Foundation of Jilin Province China

Research Grants Council of the Hong Kong Special Administrative Region China

National Research Foundation Prime Minister's Office Singapore

Publisher

ACM Press

Reference31 articles.

1. Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, and Petia Radeva. 2018. Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Transactions on Multimedia 20, 12 (2018), 3266--3275.

2. Oscar Beijbom, Neel Joshi, Dan Morris, Scott Saponas, and Siddharth Khullar. 2015. Menu-match: Restaurant-specific food logging from images. In 2015 IEEE Winter Conference on Applications of Computer Vision. IEEE, 844--851.

3. Vinay Bettadapura, Edison Thomaz, Aman Parnami, Gregory D Abowd, and Irfan Essa. 2015. Leveraging context to support automated food recognition in restaurants. In 2015 IEEE Winter Conference on Applications of Computer Vision. IEEE, 580--587.

4. Marc Bolaños, Aina Ferrà, and Petia Radeva. 2017. Food ingredients recognition through multi-label learning. In International Conference on Image Analysis and Processing. Springer, 394--402.

5. Marc Bolanos and Petia Radeva. 2016. Simultaneous food localization and recognition. In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 3140--3145.

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