Web-Page Color Modification for Barrier-Free Color Vision with Genetic Algorithm

In this paper, we propose a color modification scheme for web-pages described by HTML markup language in order to realize barrier-free color vision on the internet. First, we present an abstracted image model, which describes a color image as a combination of several regions divided with color information, and define some mutual color relations between regions. Next, based on fundamental research on the anomalous color vision, we design some fitness functions to modify colors in a web-page properly and effectively. Then we solve the color modification problem, which contains complex mutual color relations, by using Genetic Algorithm. Experimental results verify that the proposed scheme can make the colors in a web-page more recognizable for anomalous vision users through not only computer simulation but also psychological experiments with them.

[1]  Tatsuo Sugimura,et al.  Cooperative model for genetic operators to improve GAs , 1999, Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446).

[2]  Joel Pokorny,et al.  Congenital and acquired color vision defects , 1979 .

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  Kiyoshi Tanaka,et al.  Genetic Algorithms on NK-Landscapes: Effects of Selection, Drift, Mutation, and Recombination , 2003, EvoWorkshops.

[5]  J. Pokorny,et al.  Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm , 1975, Vision Research.

[6]  Kiyoshi Tanaka,et al.  Accelerated halftoning technique using improved genetic algorithm with tiny populations , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[7]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[8]  Kiyoshi Tanaka,et al.  Mutation strategy improves GAs performance on epistatic problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Andrew S. Glassner,et al.  Principles of Digital Image Synthesis , 1995 .

[11]  Kiyoshi Tanaka,et al.  Halftone Image Generation with Improved Multiobjective Genetic Algorithm , 2001, EMO.