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Appropriate color mapping for categorical data visualization can help with the discovery of root data patterns and effectively bring out visual aesthetics. We provide an easy way to automatically produce a coloring that matches the reference and a reference image, while still allowing classes to be clearly identified. Given an input categorical data visualization and a reference picture, we suggest an effective way to automatically produce a coloring that mimics the reference. We produce a color palette with a significant distance between the colors obtained by showcasing dominant and discriminable hues from the image's color space. These colors are assigned to specific classes by an integer quadratic scheme to increase the point distinctness of the given chart while keeping the color coordinates in the source image. Results on various coloring tasks have been displayed, with a variety of new coloring appearances for the input data. After using our equipment, user feedback reveals that our system does not have a way to automatically produce desirable colors that satisfy the user's needs when choosing a reference.
Source link: https://doi.org/10.1007/s41095-021-0258-0
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