Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised 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 33000 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.

该研究提供了自动工具，可通过每日拍摄的照片流来个性化分析人们的健康习惯，并提出了一种新的自动分类方法来分类与食物相关的环境，同时构建了一个具有语义层次结构的模型，并介绍了一个超过33000张图像的数据集，其分类准确度和F分数分别为56％和65％，明显优于基线方法。

基于分层方法对自拍照流中的食品场景进行分类