Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2021
Abstract
Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks—the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images
Keywords
Datasets, Food Computing, Semantic Segmentation, Deep Learning
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Virtual, October 20-24
First Page
506
Last Page
515
ISBN
9781450386517
Identifier
10.1145/3474085.3475201
Publisher
ACM
City or Country
New York
Citation
WU, Xiongwei; FU, Xin; LIU, Ying; LIM, Ee-peng; HOI, Steven C. H.; and SUN, Qianru.
A large-scale benchmark for food image segmentation. (2021). MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Virtual, October 20-24. 506-515.
Available at: https://ink.library.smu.edu.sg/sis_research/6269
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3474085.3475201
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Numerical Analysis and Scientific Computing Commons
