Prediction of Visibility of Two Colors Schemes Using the Convolutional Neural Networks

Shodai Sasaki, Yoshihisa Shinozawa

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルHCI International 2022 Posters - 24th International Conference on Human-Computer Interaction, HCII 2022, Proceedings
編集者Constantine Stephanidis, Margherita Antona, Stavroula Ntoa
出版社Springer Science and Business Media Deutschland GmbH
ページ218-226
ページ数9
ISBN(印刷版)9783031063879
DOI
出版ステータスPublished - 2022
イベント24th International Conference on Human-Computer Interaction, HCI International, HCII 2022 - Virtual, Online
継続期間: 2022 6月 262022 7月 1

出版物シリーズ

名前Communications in Computer and Information Science
1581 CCIS
ISSN(印刷版)1865-0929
ISSN(電子版)1865-0937

Conference

Conference24th International Conference on Human-Computer Interaction, HCI International, HCII 2022
CityVirtual, Online
Period22/6/2622/7/1

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 数学 (全般)

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