TY - GEN
T1 - Prediction of Visibility of Two Colors Schemes Using the Convolutional Neural Networks
AU - Sasaki, Shodai
AU - Shinozawa, Yoshihisa
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this study, we propose a method to predict the visibility of images composed of different color schemes using Convolutional Neural Networks (CNN), which introduces the Multi-Stage Color Model (MSC Model), a human color vision model. Using the MSC model, a method has been proposed to predict the visibility of color schemes on a computer by reproducing the way humans see colors. In this study, the response values of multiple cell layers by the MSC model are used as input values to the CNN. In constructing the CNN, we propose a CNN with an improved structure to extract the features for the visibility between images, and attempt to improve the prediction accuracy of visibility. The CNN is trained on the color scheme visibility data collected in a pairwise comparison experiment, and the trained CNN is used to predict visibility for unknown color schemes. The results show that the construction of a CNN with the MSC model improves the accuracy of color scheme visibility.
AB - In this study, we propose a method to predict the visibility of images composed of different color schemes using Convolutional Neural Networks (CNN), which introduces the Multi-Stage Color Model (MSC Model), a human color vision model. Using the MSC model, a method has been proposed to predict the visibility of color schemes on a computer by reproducing the way humans see colors. In this study, the response values of multiple cell layers by the MSC model are used as input values to the CNN. In constructing the CNN, we propose a CNN with an improved structure to extract the features for the visibility between images, and attempt to improve the prediction accuracy of visibility. The CNN is trained on the color scheme visibility data collected in a pairwise comparison experiment, and the trained CNN is used to predict visibility for unknown color schemes. The results show that the construction of a CNN with the MSC model improves the accuracy of color scheme visibility.
KW - Color scheme
KW - Convolutional Neural Networks
KW - Multi-Stage Color Model
UR - http://www.scopus.com/inward/record.url?scp=85133215862&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-06388-6_29
DO - 10.1007/978-3-031-06388-6_29
M3 - Conference contribution
AN - SCOPUS:85133215862
SN - 9783031063879
T3 - Communications in Computer and Information Science
SP - 218
EP - 226
BT - HCI International 2022 Posters - 24th International Conference on Human-Computer Interaction, HCII 2022, Proceedings
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Human-Computer Interaction, HCI International, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -