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An affect-based classification of emotions associated with images of food

Food and emotions are correlated. Recent research on the relationship between foods and emotions mainly focused on identifying emotions when viewing food images. The studies try to find image attributes that evoke food-related emotions. We concentrate on affective image classification and investigat...

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Main Authors: Tahir, Yusra, Rahman, Anis Ur, Ravana, Sri Devi
Format: Article
Published: Springer Verlag 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/27116/
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spelling my.um.eprints.271162022-05-24T01:52:42Z http://eprints.um.edu.my/27116/ An affect-based classification of emotions associated with images of food Tahir, Yusra Rahman, Anis Ur Ravana, Sri Devi QA75 Electronic computers. Computer science Food and emotions are correlated. Recent research on the relationship between foods and emotions mainly focused on identifying emotions when viewing food images. The studies try to find image attributes that evoke food-related emotions. We concentrate on affective image classification and investigate the performance of different features in a food-related emotion classification framework. First, we extract features of different levels for each food image. Very basic low-level features and art features derived from principle-of-art features are extracted as mid-level features. Then, we develop models for valence-arousal affect dimensions trained using different machine learning techniques. Extensive experiments are conducted on a combined food image dataset. The results demonstrate the effectiveness of the proposed food-related emotion classification method. The results demonstrate the effectiveness of the proposed food-related emotion classification model by comparing different classifiers for the two affect dimensions (valence and arousal), resulting in an accuracy of 67% and 88% respectively. Springer Verlag 2021-02 Article PeerReviewed Tahir, Yusra and Rahman, Anis Ur and Ravana, Sri Devi (2021) An affect-based classification of emotions associated with images of food. Journal of Food Measurement and Characterization, 15 (1). pp. 519-530. ISSN 2193-4126, DOI https://doi.org/10.1007/s11694-020-00650-7 <https://doi.org/10.1007/s11694-020-00650-7>. 10.1007/s11694-020-00650-7
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Tahir, Yusra
Rahman, Anis Ur
Ravana, Sri Devi
An affect-based classification of emotions associated with images of food
description Food and emotions are correlated. Recent research on the relationship between foods and emotions mainly focused on identifying emotions when viewing food images. The studies try to find image attributes that evoke food-related emotions. We concentrate on affective image classification and investigate the performance of different features in a food-related emotion classification framework. First, we extract features of different levels for each food image. Very basic low-level features and art features derived from principle-of-art features are extracted as mid-level features. Then, we develop models for valence-arousal affect dimensions trained using different machine learning techniques. Extensive experiments are conducted on a combined food image dataset. The results demonstrate the effectiveness of the proposed food-related emotion classification method. The results demonstrate the effectiveness of the proposed food-related emotion classification model by comparing different classifiers for the two affect dimensions (valence and arousal), resulting in an accuracy of 67% and 88% respectively.
format Article
author Tahir, Yusra
Rahman, Anis Ur
Ravana, Sri Devi
author_facet Tahir, Yusra
Rahman, Anis Ur
Ravana, Sri Devi
author_sort Tahir, Yusra
title An affect-based classification of emotions associated with images of food
title_short An affect-based classification of emotions associated with images of food
title_full An affect-based classification of emotions associated with images of food
title_fullStr An affect-based classification of emotions associated with images of food
title_full_unstemmed An affect-based classification of emotions associated with images of food
title_sort affect-based classification of emotions associated with images of food
publisher Springer Verlag
publishDate 2021
url http://eprints.um.edu.my/27116/
_version_ 1735409501558800384
score 13.211869

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