Extrapolating continuous color emotions through deep learning
Details
Download: Ram_etal_2020_Physical_Review.pdf (2184.49 [Ko])
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_4D0E6E50629D
Type
Article: article from journal or magazin.
Publication sub-type
Minutes: analyse of a published work.
Collection
Publications
Institution
Title
Extrapolating continuous color emotions through deep learning
Journal
Physical Review Research
ISSN
2643-1564
Publication state
Published
Issued date
09/2020
Peer-reviewed
Oui
Volume
2
Number
3
Pages
033350
Language
english
Abstract
By means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) 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 (type-m individuals) typically associate a given emotion with darker colors, while females (type-f individuals) associate it 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.
Keywords
colour, emotion, machine learning, neural network
Open Access
Yes
Funding(s)
Swiss National Science Foundation / Projects / 100014_182138
Swiss National Science Foundation / Careers / P0LAP1_175055
Create date
03/09/2020 12:34
Last modification date
09/09/2020 6:08