ArXiv Preprint
Localized food datasets have profound meaning in revealing a country's
special cuisines to explore people's dietary behaviors, which will shed light
on their health conditions and disease development. In this paper, revolving
around the demand for accurate food recognition in Singapore, we develop the
FoodSG platform to incubate diverse healthcare-oriented applications as a
service in Singapore, taking into account their shared requirements. We release
a localized Singaporean food dataset FoodSG-233 with a systematic cleaning and
curation pipeline for promoting future data management research in food
computing. To overcome the hurdle in recognition performance brought by
Singaporean multifarious food dishes, we propose to integrate supervised
contrastive learning into our food recognition model FoodSG-SCL for the
intrinsic capability to mine hard positive/negative samples and therefore boost
the accuracy. Through a comprehensive evaluation, we share the insightful
experience with practitioners in the data management community regarding
food-related data-intensive healthcare applications.
The FoodSG-233 dataset can be accessed via: https://foodlg.comp.nus.edu.sg/.
Kaiping Zheng, Thao Nguyen, Jesslyn Hwei Sing Chong, Charlene Enhui Goh, Melanie Herschel, Hee Hoon Lee, Beng Chin Ooi, Wei Wang, James Yip
2023-01-10
