Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams

Autor: Nicolai Petkov, Petia Radeva, Domenec Puig, Md. Mostafa Kamal Sarker, Estefanía Talavera Martínez, Maria Leyva-Vallina
Přispěvatelé: Intelligent Systems
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Food intake
lifestyle
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
Wearable computer
Context (language use)
02 engineering and technology
Semantic hierarchy
scenes classification
010501 environmental sciences
Machine learning
computer.software_genre
Semantics
01 natural sciences
Machine Learning
Health Information Management
0202 electrical engineering
electronic engineering
information engineering
Image Processing
Computer-Assisted
Photography
Humans
Electrical and Electronic Engineering
Egocentric vision
Life Style
0105 earth and related environmental sciences
2. Zero hunger
business.industry
Computer Science Applications
Visualization
Food
food scenes
Nutritional behavior
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Algorithms
Biotechnology
Zdroj: IEEE Journal of Biomedical and Health Informatics, 24(3):8735865, 866-877. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE Journal of Biomedical and Health Informatics
ISSN: 2168-2208
2168-2194
DOI: 10.1109/JBHI.2019.2922390
Popis: 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.
Databáze: OpenAIRE
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