UEC-FoodPix Complete: A Large-Scale Food Image Segmentation Dataset

• Published in: Pattern Recognition. ICPR International Workshops and Challenges Vol. 12665; pp. 647 - 659
• Main Authors: Okamoto, Kaimu, Yanai, Keiji
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Calorie estimation; Food image; Food image synthesis; Semantic segmentation
• ISBN: 3030688208; 9783030688202
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-68821-9_51

Currently, many segmentation image datasets are open to the public. However, only a few open segmentation image dataset of food images exists. Among them, UEC-FoodPix is a large-scale food image segmentation dataset which consists of 10,000 food images with segmentation masks. However, it contains some incomplete mask images, because most of the segmentation masks were generated automatically based on the bounding boxes. To enable accurate food segmentation, complete segmentation masks are required for training. Therefore, in this work, we created “UEC-FoodPix Complete” by refining the 9,000 segmentation masks by hand which were automatically generated in the previous UEC-FoodPix. As a result, the segmentation performance was much improved compared to the segmentation model trained with the original UEC-FoodPix. In addition, as applications of the new food segmentation dataset, we performed food calorie estimation using the food segmentation models trained with “UEC-FoodPix Complete”, and food image synthesis from segmentation masks.