A large-scale benchmark for food image segmentation

• Main Authors: WU, Xiongwei, FU, Xin, LIU, Ying, LIM, Ee-peng, HOI, Steven C. H., SUN, Qianru
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Datasets; Food Computing; Semantic Segmentation; Deep Learning; Databases and Information Systems; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6269; https://ink.library.smu.edu.sg/context/sis_research/article/7272/viewcontent/FoodSeg_MM2021_Camera_Ready.pdf
• Institution: Singapore Management University

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