Hierarchical approach to classify food scenes in egocentric photo-streams
This work provides incremental tools for personalized health monitoring by analyzing eating contexts from wearable camera data.
The paper tackles the problem of automatically classifying food-related environments from egocentric photo-streams to analyze personal health habits, achieving 56% accuracy and 65% F-score, which outperforms baseline methods.
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.