Automatic Chinese Food recognition based on a stacking fusion model

Bokun Fan1; Weiqi Li2; Liang Dong3; Jingzhen Li4; Zedong Nie4

10.1109/EMBC40787.2023.10340620

2023

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

2375-7477

1558-4615

979-8-3503-2448-8

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

1-4

24-27 July 2023

Sydney, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

With commercialization of deep learning models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of Deep-learning techniques on visual recognition tasks and proposed a big-data-driven Deep-learning model regressing from food images. We established the largest data set of Chinese dishes to date, named CNFOOD-241. It contained more than 190,000 images with 241 categories, covering Staple food, meat, vegetarian diet, mixed meat and vegetables, soups, dessert category. This study also compares the prediction results of three popular deep learning models on this dataset, ResNeXt101_32x32d achieving up to 82.05% for top-1 accuracy and 97.13% for top-5 accuracy. Besides, this paper uses a multi-model fusion method based on stacking in the field of food recognition for the first time. We built a meta-learner after the base model to integrate the three base models of different architectures to improve robustness. The accuracy achieves 82.88% for top-1 accuracy.

Deep learning Obesity Cloud computing Visualization Computational modeling Stacking Data models

其他

英语

EI ; CPCI-S

National Key R&D Program of China[2022YFB3203702] ; National Natural Science Foundation of China[62173318]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical

WOS:001133788302198

20240215361964

Learning systems ; Meats

Ergonomics and Human Factors Engineering:461.4 ; Food Products:822.3

IEEE

1.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2.Southern University of Science and Technology, Shenzhen, China
3.Hunan University, Changsha, China
4.Research Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, Shenzhen, China

Bokun Fan,Weiqi Li,Liang Dong,et al. Automatic Chinese Food recognition based on a stacking fusion model[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-4.