Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/23246
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://hdl.handle.net/20.500.14279/23246 | ISSN: | 26431564 | 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 of Athens Kadir Has University University of Lausanne |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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File | Description | Size | Format | |
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PhysRevResearch.2.033350.pdf | 2.18 MB | Adobe PDF | View/Open |
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