Hierarchical approach to classify food scenes in egocentric photo-streams

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ID: 282377
2019
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Abstract
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.
Reference Key
radeva2019hierarchical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva
Journal arXiv
Year 2019
DOI
DOI not found
URL
http://arxiv.org/abs/1905.04097v1
Keywords
cs.cv

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