论文标题
利用自动个性化营养：基于营养分类法的食物图像识别基准和数据集
Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy
论文作者
论文摘要
在当今的久坐社会中，维持健康的生活方式变得越来越具有挑战性。为了解决这个问题，国家和国际组织都做出了许多努力，以促进更健康的饮食和增加体育锻炼。但是，在日常生活中实施这些建议可能很困难，因为它们通常是通用的，而不是针对个人量身定制的。这项研究介绍了AI4Food-NutritionDB数据库，这是第一个基于国家和国际卫生当局的建议，结合食物图像和营养分类的营养数据库。该数据库提供了多层分类，包括6个营养水平，19个主要类别（例如“肉”），73个子类别（例如，“白肉”）和893种特定的食品（例如，“鸡肉”）。 AI4Food-NutritionDB在食品摄入频率，质量和分类方面为新食物计算方法打开了大门。此外，我们提出了标准化的实验方案和基准，包括基于营养分类法（即类别，子类别和最终产品识别）的三个任务。这些资源可用于研究社区，包括我们对AI4Food-NutritionDB培训的深度学习模型，该模型可以用作预培训的模型，从而获得精确的识别结果，以挑战食品图像数据库。
Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases.
