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Research Article | Open Access

Image-guided color mapping for categorical data visualization

Suzhou University of Science and Technology, Suzhou 215009, China
Shenzhen University, Shenzhen 518052, China
The University of Haifa, Haifa 3498838, Israel
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Graphical Abstract

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 palettediscriminabilityimage-guidedcategorical data visualization

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41095_0258_ESM.mp4

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[1]
Behrisch, M.; Blumenschein, M.; Kim, N. W.; Shao, L.; El-Assady, M.; Fuchs, J.; Seebacher, D.; Diehl, A.; Brandes, U.; Pfister, H.; Schreck, T.; Weiskopf, D.; Keim, D. A. Quality metrics for information visualization. Computer Graphics Forum Vol. 37, No. 3, 625-662, 2018.
Crossref Google Scholar
[2]
Silva, S.; Sousa Santos, B.; Madeira, J. Using color in visualization: A survey. Computers & Graphics Vol. 35, No. 2, 320-333, 2011.
Crossref Google Scholar
[3]
Harrower, M.; Brewer, C. A. ColorBrewer.org: An online tool for selecting colour schemes for maps. The Cartographic Journal Vol. 40, No. 1, 27-37, 2003.
Crossref Google Scholar
[4]
Gramazio, C. C.; Laidlaw, D. H.; Schloss, K. B. Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 521-530, 2017.
Crossref Google Scholar
[5]
Lin, S.; Fortuna, J.; Kulkarni, C.; Stone, M.; Heer, J. Selecting semantically-resonant colors for data visualization. Computer Graphics Forum Vol. 32, No. 3pt4, 401-410, 2013.
Crossref Google Scholar
[6]
Kim, H. R.; Yoo, M. J.; Kang, H.; Lee, I. K.Perceptually-based color assignment. Computer Graphics Forum Vol. 33, No. 7, 309-318, 2014.
Crossref Google Scholar
[7]
Wang, Y.; Chen, X.; Ge, T.; Bao, C.; Sedlmair, M.; Fu, C. W.; Deussen, O.; Chen, B. Optimizing color assignment for perception of class separability in multiclass scatterplots. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 820-829, 2019.
Crossref Google Scholar
[8]
Lin, S.; Ritchie, D.; Fisher, M.; Hanrahan, P. Probabilistic color-by-numbers. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 37, 2013.
Crossref Google Scholar
[9]
Lu, K. C.; Feng, M.; Chen, X.; Sedlmair, M.; Deussen, O.; Lischinski, D.; Cheng, Z.; Wang, Y. Palettailor: Discriminable colorization for categorical data. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 475-484, 2021.
Crossref Google Scholar
[10]
Schlömer, T.; Heck, D.; Deussen, O. Farthest-point optimized point sets with maximized minimum distance. In: Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics, 135-142, 2011.
Crossref
[11]
Healey, C. G. Choosing effective colours for data visualization. In: Proceedings of the 7th Annual IEEE Visualization, 263-270, 1996.
[12]
Trumbo, B. E. A theory for coloring bivariate statistical maps. The American Statistician Vol. 35, No. 4, 220-226, 1981.
Crossref Google Scholar
[13]
Zeileis, A.; Hornik, K.; Murrell, P. Escaping RGBland: Selecting colors for statistical graphics. Computational Statistics & Data Analysis Vol. 53, No. 9, 3259-3270, 2009.
Crossref Google Scholar
[14]
Bartram, L.; Patra, A.; Stone, M. Affective color in visualization. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, 1364-1374, 2017.
Crossref
[15]
O’Donovan, P.; Agarwala, A.; Hertzmann, A. Color compatibility from large datasets. ACM Transactions on Graphics Vol. 30, No. 4, Article No. 63, 2011.
Crossref Google Scholar
[16]
Kita, N.; Miyata, K. Aesthetic rating and color suggestion for color palettes. Computer Graphics Forum Vol. 35, No. 7, 127-136, 2016.
Crossref Google Scholar
[17]
Fang, H.; Walton, S.; Delahaye, E.; Harris, J.; Storchak, D. A.; Chen, M. Categorical colormap optimization with visualization case studies. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 871-880, 2017.
Crossref Google Scholar
[18]
Lin, S.; Hanrahan, P. Modeling how people extract color themes from images. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 3101-3110, 2013.
Crossref
[19]
Poco, J.; Mayhua, A.; Heer, J. Extracting and retargeting color mappings from bitmap images of visualizations. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 637-646, 2018.
Crossref Google Scholar
[20]
Chang, H. W.; Fried, O.; Liu, Y. M.; DiVerdi, S.; Finkelstein, A. Palette-based photo recoloring. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 139, 2015.
Crossref Google Scholar
[21]
Tan, J. C.; Lien, J. M.; Gingold, Y. Decomposing images into layers via RGB-space geometry. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 7, 2017.
Crossref Google Scholar
[22]
Tan, J. C.; Echevarria, J.; Gingold, Y. Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry. ACM Transactions on Graphics Vol. 37, No. 6, Article No. 262, 2018.
Crossref Google Scholar
[23]
Aksoy, Y.; Aydin, T. O.; Smolić, A.; Pollefeys, M. Unmixing-based soft color segmentation for image manipulation. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 19, 2017.
Crossref Google Scholar
[24]
Zhang, Q.; Xiao, C. X.; Sun, H. Q.; Tang, F. Palette-based image recoloring using color decomposition optimization. IEEE Transactions on Image Processing Vol. 26, No. 4, 1952-1964, 2017.
Crossref Google Scholar
[25]
Nguyen, R. M. H.; Price, B.; Cohen, S.; Brown, M. S. Group-theme recoloring for multi-image color consistency. Computer Graphics Forum Vol. 36, No. 7, 83-92, 2017.
Crossref Google Scholar
[26]
Phan, H. Q.; Fu, H. B.; Chan, A. B. Color orchestra: Ordering color palettes for interpolation and prediction. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 6, 1942-1955, 2018.
Crossref Google Scholar
[27]
Setlur, V.; Stone, M. C. A linguistic approach to categorical color assignment for data visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 698-707, 2016.
Crossref Google Scholar
[28]
Lee, S.; Sips, M.; Seidel, H. P. Perceptually driven visibility optimization for categorical data visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 10, 1746-1757, 2013.
Crossref Google Scholar
[29]
Bohra, M.; Gandhi, V. ColorArt: Suggesting colorizations for graphic arts using optimal color-graph matching. In: Proceedings of the Graphics Interface, 95-102, 2020.
[30]
Sharma, G.; Wu, W. C.; Dalal, E. N. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application Vol. 30, No. 1, 21-30, 2005.
Crossref Google Scholar
[31]
Stone, M.; Szafir, D. A.; Setlur, V. An engineering model for color difference as a function of size. In: Proceedings of the Color and Imaging Conference, 253-258, 2014.
[32]
Leordeanu, M.; Hebert, M.; Sukthankar, R. An integer projected fixed point method for graph matching and MAP inference. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, 1114-1122, 2009.
[33]
Bridson, R. Fast Poisson disk sampling in arbitrary dimensions. In: Proceedings of the ACM SIGGRAPH Sketches, 22-es, 2007.
Crossref
[34]
Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE Vol. 86, No. 11, 2278-2324, 1998.
Crossref Google Scholar
[35]
Van der Maaten, L.; Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Research Vol. 9, No. 86, 2579-2605, 2008.
Google Scholar
[36]
Shapira, L.; Shamir, A.; Cohen-Or, D. Image appearance exploration by model-based navigation. Computer Graphics Forum Vol. 28, No. 2, 629-638, 2009.
Crossref Google Scholar
[37]
TinEye. Color extraction. 2021. Available at https://labs.tineye.com/color/.
[38]
Shugrina, M.; Kar, A.; Fidler, S.; Singh, K. Nonlinear color triads for approximation, learning and direct manipulation of color distributions. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 97, 2020.
Crossref Google Scholar
Computational Visual Media
Volume 8 Issue 4,
December 2022
Pages 613-629
DOI: 10.1007/s41095-021-0258-0
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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Received: 01 July 2021
Accepted: 01 October 2021
Published: 27 May 2022
© The Author(s) 2022.

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