Allegra Dario, Battiato Sebastiano, Ortis Alessandro, Urso Salvatore, Polosa Riccardo
Department of Mathematics and Computer Science.
Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Catania, Italy.
Health Psychol Res. 2020 Dec 30;8(3):9297. doi: 10.4081/hpr.2020.9297.
Food understanding from digital media has become a challenge with important applications in many different domains. On the other hand, food is a crucial part of human life since the health is strictly affected by diet. The impact of food in people life led Computer Vision specialists to develop new methods for automatic food intake monitoring and food logging. In this review paper we provide an overview about automatic food intake monitoring, by focusing on technical aspects and Computer Vision works which solve the main involved tasks (, classification, recognitions, segmentation, etc.). Specifically, we conducted a systematic review on main scientific databases, including interdisciplinary databases (., Scopus) as well as academic databases in the field of computer science that focus on topics related to image understanding (, recognition, analysis, retrieval). The search queries were based on the following key words: "food recognition", "food classification", "food portion estimation", "food logging" and "food image dataset". A total of 434 papers have been retrieved. We excluded 329 works in the first screening and performed a new check for the remaining 105 papers. Then, we manually added 5 recent relevant studies. Our final selection includes 23 papers that present systems for automatic food intake monitoring, as well as 46 papers which addressed Computer Vision tasks related food images analysis which we consider essential for a comprehensive overview about this research topic. A discussion that highlights the limitations of this research field is reported in conclusions.
从数字媒体中理解食物已成为一项具有挑战性的任务，在许多不同领域都有重要应用。另一方面，食物是人类生活的关键部分，因为健康受到饮食的严格影响。食物对人们生活的影响促使计算机视觉专家开发用于自动监测食物摄入量和记录食物日志的新方法。在这篇综述论文中，我们通过关注技术方面以及解决主要相关任务（分类、识别、分割等）的计算机视觉工作，对自动食物摄入量监测进行了概述。具体来说，我们对主要科学数据库进行了系统综述，包括跨学科数据库（如Scopus）以及计算机科学领域中专注于与图像理解（识别、分析、检索）相关主题的学术数据库。搜索查询基于以下关键词：“食物识别”、“食物分类”、“食物份量估计”、“食物日志记录”和“食物图像数据集”。总共检索到434篇论文。在首次筛选中，我们排除了329篇作品，并对其余105篇论文进行了新的检查。然后，我们手动添加了5篇近期相关研究。我们的最终选择包括23篇介绍自动食物摄入量监测系统的论文，以及46篇涉及与食物图像分析相关的计算机视觉任务的论文，我们认为这些对于全面概述该研究主题至关重要。结论部分报告了突出该研究领域局限性的讨论。
