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Computational Visual Media 2022, 8(4): 613-629 https://doi.org/10.1007/s41095-021-0258-0
Research Article | Open Access | Issue | Published: 27 May 2022

Image-guided color mapping for categorical data visualization

Show Author's Information Qian Zheng1,  Min Lu2,  Sicong Wu2,  Ruizhen Hu2,  Joel Lanir3,  Hui Huang2( )
Suzhou University of Science and Technology, Suzhou 215009, China
Shenzhen University, Shenzhen 518052, China
The University of Haifa, Haifa 3498838, Israel
Keywords:
color palette, discriminability, image-guided, categorical data visualization
Cite this article:
Zheng Q, Lu M, Wu S, et al. Image-guided color mapping for categorical data visualization. Computational Visual Media, 2022, 8(4): 613-629. https://doi.org/10.1007/s41095-021-0258-0
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Abstract Full text Electronic supplementary material About this article

Appropriate color mapping for categorical data visualization can significantly facilitate the discovery of underlying data patterns and effectively bring out visual aesthetics. Some systems suggest pre-defined palettes for this task. However, a predefined color mapping is not always optimal, failing to consider users’ needs for customization. Given an input cate-gorical data visualization and a reference image, we present an effective method to automatically generate a coloring that resembles the reference while allowing classes to be easily distinguished. We extract a color palette with high perceptual distance between the colors by sampling dominant and discriminable colors from the image’s color space. These colors are assigned to given classes by solving an integer quadratic program to optimize point distinctness of the given chart while preserving the color spatial relations in the source image. We show results on various coloring tasks, with a diverse set of new coloring appearances for the input data. We also compare our approach to state-of-the-art palettes in a controlled user study, which shows that our method achieves comparable performance in class discrimination, while being more similar to the source image. User feedback after using our system verifies its efficiency in automatically generating desirable colorings that meet the user’s expectations when choosing a reference.


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Electronic supplementary material
About this article

Image-guided color mapping for categorical data visualization

Show Author's information Qian Zheng1, Min Lu2, Sicong Wu2, Ruizhen Hu2, Joel Lanir3, Hui Huang2( )
Suzhou University of Science and Technology, Suzhou 215009, China
Shenzhen University, Shenzhen 518052, China
The University of Haifa, Haifa 3498838, Israel

Abstract

Appropriate color mapping for categorical data visualization can significantly facilitate the discovery of underlying data patterns and effectively bring out visual aesthetics. Some systems suggest pre-defined palettes for this task. However, a predefined color mapping is not always optimal, failing to consider users’ needs for customization. Given an input cate-gorical data visualization and a reference image, we present an effective method to automatically generate a coloring that resembles the reference while allowing classes to be easily distinguished. We extract a color palette with high perceptual distance between the colors by sampling dominant and discriminable colors from the image’s color space. These colors are assigned to given classes by solving an integer quadratic program to optimize point distinctness of the given chart while preserving the color spatial relations in the source image. We show results on various coloring tasks, with a diverse set of new coloring appearances for the input data. We also compare our approach to state-of-the-art palettes in a controlled user study, which shows that our method achieves comparable performance in class discrimination, while being more similar to the source image. User feedback after using our system verifies its efficiency in automatically generating desirable colorings that meet the user’s expectations when choosing a reference.

Keywords: color palette, discriminability, image-guided, categorical data visualization

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Publication history

Received: 01 July 2021
Accepted: 01 October 2021
Published: 27 May 2022
Issue date: December 2022

Copyright

© The Author(s) 2022.

Acknowledgements

We thank the reviewers for their valuable comments andconstructive suggestions. This work was supported in parts by National Natural Science Foundation of China (U2001206, 61872250), GD Talent Program (2019JC05X328), GD Natural Science Foundation (2020A0505100064, 2021B1515020085), DEGP Key Project (2018KZDXM058), and Shenzhen Science and Technology Key Program (RCJC20200714114435012, JCYJ20210324120213036).

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