Recent studies have shown that the environment where people eat can affect their nutritional behavior [1]. In this paper, we provide automatic tools for personalized analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33 000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56% and 65%, respectively, clearly outperforming the baseline methods.

中文翻译：

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在以自我为中心的照片流中对食物场景进行分类的分层方法

最近的研究表明，人们进食的环境会影响他们的营养行为[1]。在本文中，我们提供了自动工具，通过检查每天记录的以自我为中心的照片流，可以对一个人的健康习惯进行个性化分析。具体而言，我们提出了一种用于食品相关环境分类的新自动方法，该方法能够对多达15个此类场景进行分类。这样，人们可以监控食物摄入量的背景情况，从而客观地了解自己的日常饮食习惯。我们提出了一个模型，该模型对以语义层次结构组织的与食物相关的场景进行分类。此外，我们提出并提供了一个新的以自我为中心的数据集，该数据集由可穿戴式摄像机记录的超过33 000张图像组成，并在其上测试了我们提出的模型。