Department of Nutrition Science, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA.
School of Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN 47907, USA.
Nutrients. 2023 Jul 18;15(14):3183. doi: 10.3390/nu15143183.
New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.
开发用于识别食物的新型成像技术可以减轻参与者的报告负担，但严重依赖于与之相关联的食物图像数据库的质量，以准确识别食物图像。本研究的目的是开发一种基于美国最常见食物和对能量贡献最大的食物的方法来创建食物图像数据库。目标包括使用基于标准化美国农业部（USDA）“我们吃什么在美国”（WWEIA）食物分类系统的食物系统分类结构，该系统最终可用于将食物图像链接到营养成分数据库，即美国农业部食物和营养数据库膳食研究（FNDDS）。使用从网络中挖掘的图像构建食物图像数据库，这些图像配有边界框，经过识别、注释，然后根据与美国农业部 WWEIA 对齐的分类进行组织。图像按食物类别和子类进行分类，然后在 USDA 的 FNDDS 中分配相应的 USDA 食物代码，以便系统地组织食物图像并促进与营养成分的链接。由此产生的食物图像数据库可用于食物识别和饮食评估。
