Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/23246
Title: Extrapolating continuous color emotions through deep learning
Authors: Ram, Vishaal 
Schaposnik, Laura P. 
Konstantinou, Nikos 
Volkan, Eliz 
Papadatou-Pastou, Marietta 
Manav, Banu 
Jonauskaite, Domicele 
Mohr, Christine 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Deep learning;Quantitative Biology;Quantitative Methods;Computer Science;Biological Physics;Physics;Neural network
Issue Date: Nov-2020
Source: Physical Review Research, 2020, vol. 2, no. 3, articl. no. 033350
Volume: 2
Issue: 3
Journal: Physical Review Research 
Abstract: By means of an experimental dataset, we use deep learning to implement an RGB extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males typically associate a given emotion with darker colors while females with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.
URI: https://ktisis.cut.ac.cy/handle/10488/23246
ISSN: 2643-1564
DOI: 10.1103/PhysRevResearch.2.033350
Rights: © The Author(s)
Type: Article
Affiliation : University of Illinois at Chicago 
Cyprus University of Technology 
Milton High School 
National and Kapodistrian University ofAthens 
Kadir Has University 
University of Lausanne 
Appears in Collections:Άρθρα/Articles

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