Prediction of visibility for color scheme on the web browser with neural networks

Miki Yamaguchi, Yoshihisa Shinozawa

Research output: Contribution to journalArticle

Abstract

In this study, we propose neural networks for predicting the visibility of color schemes. In recent years, most of us have accessed websites owing to the spread of the Internet. It is necessary to design web pages that allow users to access information easily. The color scheme is one of the most important elements of website design and therefore, we focus on the visibility of the background and character colors in this study. The prediction methods of visibility of color scheme have been proposed. In one of the prediction methods, neural networks are used to forecast pairwise comparison tables that indicate the visibility of background and character colors. Our model employs neural networks for color recognition and visibility prediction. The neural networks used for color recognition include functions that forecast the color class name from a color and extract the features of the color. The neural networks used for visibility prediction include functions that employ the features of background and character colors extracted by neural networks for color recognition and forecast the visibility of a color scheme. Pairwise comparison tables are forecasted with the prediction results of neural networks for visibility prediction. We conducted pairwise comparison experiment on a web browser, as well as color recognition experiment and evaluated our model. The results of the experiments suggest that our model could improve the accuracy of pairwise comparison tables compared to existing methods. Thus, proposed model can be used to predict the visibility of color schemes.

Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalInternational Journal of Advanced Computer Science and Applications
Volume10
Issue number6
Publication statusPublished - 2019 Jan 1

Fingerprint

Web browsers
Visibility
Color
Neural networks
Websites
Experiments

Keywords

  • Color recognition
  • Human color vision
  • Neural networks
  • Pairwise comparison experiment
  • Visibility prediction

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Prediction of visibility for color scheme on the web browser with neural networks. / Yamaguchi, Miki; Shinozawa, Yoshihisa.

In: International Journal of Advanced Computer Science and Applications, Vol. 10, No. 6, 01.01.2019, p. 9-18.

Research output: Contribution to journalArticle

@article{6c9c5e965a864e83903d6b6d3233fb07,
title = "Prediction of visibility for color scheme on the web browser with neural networks",
abstract = "In this study, we propose neural networks for predicting the visibility of color schemes. In recent years, most of us have accessed websites owing to the spread of the Internet. It is necessary to design web pages that allow users to access information easily. The color scheme is one of the most important elements of website design and therefore, we focus on the visibility of the background and character colors in this study. The prediction methods of visibility of color scheme have been proposed. In one of the prediction methods, neural networks are used to forecast pairwise comparison tables that indicate the visibility of background and character colors. Our model employs neural networks for color recognition and visibility prediction. The neural networks used for color recognition include functions that forecast the color class name from a color and extract the features of the color. The neural networks used for visibility prediction include functions that employ the features of background and character colors extracted by neural networks for color recognition and forecast the visibility of a color scheme. Pairwise comparison tables are forecasted with the prediction results of neural networks for visibility prediction. We conducted pairwise comparison experiment on a web browser, as well as color recognition experiment and evaluated our model. The results of the experiments suggest that our model could improve the accuracy of pairwise comparison tables compared to existing methods. Thus, proposed model can be used to predict the visibility of color schemes.",
keywords = "Color recognition, Human color vision, Neural networks, Pairwise comparison experiment, Visibility prediction",
author = "Miki Yamaguchi and Yoshihisa Shinozawa",
year = "2019",
month = "1",
day = "1",
language = "English",
volume = "10",
pages = "9--18",
journal = "International Journal of Advanced Computer Science and Applications",
issn = "2158-107X",
publisher = "Science and Information Organization",
number = "6",

}

TY - JOUR

T1 - Prediction of visibility for color scheme on the web browser with neural networks

AU - Yamaguchi, Miki

AU - Shinozawa, Yoshihisa

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In this study, we propose neural networks for predicting the visibility of color schemes. In recent years, most of us have accessed websites owing to the spread of the Internet. It is necessary to design web pages that allow users to access information easily. The color scheme is one of the most important elements of website design and therefore, we focus on the visibility of the background and character colors in this study. The prediction methods of visibility of color scheme have been proposed. In one of the prediction methods, neural networks are used to forecast pairwise comparison tables that indicate the visibility of background and character colors. Our model employs neural networks for color recognition and visibility prediction. The neural networks used for color recognition include functions that forecast the color class name from a color and extract the features of the color. The neural networks used for visibility prediction include functions that employ the features of background and character colors extracted by neural networks for color recognition and forecast the visibility of a color scheme. Pairwise comparison tables are forecasted with the prediction results of neural networks for visibility prediction. We conducted pairwise comparison experiment on a web browser, as well as color recognition experiment and evaluated our model. The results of the experiments suggest that our model could improve the accuracy of pairwise comparison tables compared to existing methods. Thus, proposed model can be used to predict the visibility of color schemes.

AB - In this study, we propose neural networks for predicting the visibility of color schemes. In recent years, most of us have accessed websites owing to the spread of the Internet. It is necessary to design web pages that allow users to access information easily. The color scheme is one of the most important elements of website design and therefore, we focus on the visibility of the background and character colors in this study. The prediction methods of visibility of color scheme have been proposed. In one of the prediction methods, neural networks are used to forecast pairwise comparison tables that indicate the visibility of background and character colors. Our model employs neural networks for color recognition and visibility prediction. The neural networks used for color recognition include functions that forecast the color class name from a color and extract the features of the color. The neural networks used for visibility prediction include functions that employ the features of background and character colors extracted by neural networks for color recognition and forecast the visibility of a color scheme. Pairwise comparison tables are forecasted with the prediction results of neural networks for visibility prediction. We conducted pairwise comparison experiment on a web browser, as well as color recognition experiment and evaluated our model. The results of the experiments suggest that our model could improve the accuracy of pairwise comparison tables compared to existing methods. Thus, proposed model can be used to predict the visibility of color schemes.

KW - Color recognition

KW - Human color vision

KW - Neural networks

KW - Pairwise comparison experiment

KW - Visibility prediction

UR - http://www.scopus.com/inward/record.url?scp=85070488305&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85070488305&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85070488305

VL - 10

SP - 9

EP - 18

JO - International Journal of Advanced Computer Science and Applications

JF - International Journal of Advanced Computer Science and Applications

SN - 2158-107X

IS - 6

ER -