Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning techniques coupled with widely available mobile devices present new opportunities to improve the accuracy of dietary assessment that is cost-effective, convenient and timely. However, the quality and quantity of datasets are essential for achieving good performance for automated image analysis. Building a large image dataset with high quality groundtruth annotation is a challenging problem, especially for food images as the associated nutrition information needs to be provided or verified by trained dietitians with domain knowledge. In this paper, we present the design and development of a mobile, image-based dietary assessment system to capture and analyze dietary intake, which has been deployed in both controlled-feeding and community-dwelling dietary studies. Our system is capable of collecting high quality food images in naturalistic settings and provides groundtruth annotations for developing new computational approaches.
翻译:对饮食摄入的准确评估需要改进工具,以克服当前方法的局限性,包括用户负担和测量错误。新兴技术,例如利用先进的机器学习技术以及广泛可用的移动设备采用基于图像的方法,提供了新的机会来提高饮食评估的准确性,这种评估具有成本效益、方便和及时性。然而,数据集的质量和数量对于实现自动图像分析的良好性能至关重要。建立一个质量高的地面真实性注释的大型图像数据集是一个棘手问题,对于食品图像来说尤其如此,因为相关的营养信息需要由受过培训的具有领域知识的饮食学家提供或核实。在本文件中,我们介绍设计和开发一个移动的、基于图像的饮食评估系统,以捕捉和分析饮食摄入量,该系统已经用于控制饮食和社区健康饮食研究。我们的系统能够收集自然科学环境中高质量的食品图像,并为开发新的计算方法提供地面真实性说明。