Abstract
Categorical data charts composed of adjacent color blocks, such as pie charts and stack bar charts, are widely used in daily life and scientific research. Color is a significant channel in such charts. In general, colors are used to distinguish the categories of these data. In some scenes of low color accuracy, the indistinguishability of colors will make these charts challenging to read. Similar colors and poor viewing conditions make it difficult for people to read charts, and people spend more time distinguishing the boundaries of color blocks. There have been previous works by modifying the palette to achieve the distinction between colors. However, in many scenarios, people need to use a palette that meets a particular style and does not allow modification. There is also a method to optimize the color assignment with a given palette, but the method is only designed for scatter plots. In this paper, we propose an automatic coloring method for optimizing the distinguishability of blocks with a given palette based on graph theory and color science. We consider the adjacency of blocks in visual charts as a graph structure and take into account the color difference, block size, and color harmony. To demonstrate the method’s effectiveness, we compared our results with those of another color assignment method. We also use a class visibility measurement method and an aesthetic evaluation method based on deep learning to evaluate each method’s results. The results show that our method can guarantee the distinguishability of the color blocks and produce a sufficiently harmonious visualization.
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Arvind V, Das B, Köbler J, Toda S (2015) Colored hypergraph isomorphism is fixed parameter tractable. Algorithmica 71(1):120–138
Bonnici V, Giugno R, Pulvirenti A, Shasha D, Ferro A (2013) A subgraph isomorphism algorithm and its application to biochemical data. BMC Bioinform 14(7):S13
Cheng S, Xu W, Mueller K (2018) Colormap nd: a data-driven approach and tool for mapping multivariate data to color. IEEE Trans Visual Comput Gr 25(2):1361–1377
Colbourn CJ (1981) On testing isomorphism of permutation graphs. Networks 11(1):13–21
Cook SA (1971) The complexity of theorem-proving procedures. In: The third annual ACM symposium on theory of computing, pp 151–158
Cordella LP, Foggia P, Sansone C, Vento M (1999) Performance evaluation of the vf graph matching algorithm. In: Proceedings 10th international conference on image analysis and processing, pp 1172–1177
Cordella LP, Foggia P, Sansone C, Vento M (2004) A (sub) graph isomorphism algorithm for matching large graphs. IEEE Trans Pattern Anal Mach Intell 26(10):1367–1372
Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml
Fang H, Walton S, Delahaye E, Harris J, Storchak D, Chen M (2016) Categorical colormap optimization with visualization case studies. IEEE Trans Visual Comput Gr 23(1):871–880
Gramazio CC, Laidlaw DH, Schloss KB (2017) Colorgorical: creating discriminable and preferable color palettes for information visualization. IEEE Trans Visual Comput Gr 23(1):521–530
Holy T (2011) Generate maximally perceptually-distinct colors. https://ww2.mathworks.cn/matlabcentral/fileexchange/29702-generate-maximally-perceptually-distinct-colors
Hopcroft JE, Wong JK (1974) Linear time algorithm for isomorphism of planar graphs (preliminary report). In: Proceedings of the sixth annual ACM symposium on theory of computing, pp 172–184
Huertas R, Melgosa M, Hita E (2006) Influence of random-dot textures on perception of suprathreshold color differences. J Opt Soc Am A 23(9):2067–2076
Kim HR, Yoo MJ, Kang H, Lee IK (2014) Perceptually-based color assignment. Comput Gr Forum 33:309–318
Lee S, Sips M, Seidel HP (2012) Perceptually driven visibility optimization for categorical data visualization. IEEE Trans Visual Comput Gr 19(10):1746–1757
Li D, Mei H, Shen Y, Su S, Zhang W, Wang J, Zu M, Chen W (2018) Echarts: a declarative framework for rapid construction of web-based visualization. Visual Inform 2(2):136–146
Liftarn (2009) Pie chart with preliminary results from the 2004 european parliament election. https://commons.wikimedia.org/wiki/File:Pie_chart_EP_election_2004.svg
Lin S, Fortuna J, Kulkarni C, Stone M, Heer J (2013) Selecting semantically-resonant colors for data visualization. Comput Gr Forum 32:401–410
Liu S, Pei M (2018) Texture-aware emotional color transfer between images. IEEE Access 6:31375–31386
Lu K, Feng M, Chen X, Sedlmair M, Deussen O, Lischinski D, Cheng Z, Wang Y (2020) Palettailor: discriminable colorization for categorical data. IEEE Trans Visual Comput Gr 27(2):475–484
Lueker GS, Booth KS (1979) A linear time algorithm for deciding interval graph isomorphism. J ACM (JACM) 26(2):183–195
Luks EM (1982) Isomorphism of graphs of bounded valence can be tested in polynomial time. J Comput Syst Sci 25(1):42–65
McKay BD (1978) Computing automorphisms and canonical labellings of graphs. Combin Math 2:223–232
McKay BD et al. (1981) Practical graph isomorphism. Vanderbilt University Tennessee, USA, Department of Computer Science
Mokrzycki W, Tatol M (2011) Color difference delta e-a survey. Mach Gr Vis 20:383–411
Othman A, Wook TSMT, Qamar F (2020) Categorizing color appearances of image scenes based on human color perception for image retrieval. IEEE Access 8:161692–161701
Ou LC, Luo MR (2006) A colour harmony model for two-colour combinations. Color Res Appl 31(3):191–204
Setlur V, Stone MC (2015) A linguistic approach to categorical color assignment for data visualization. IEEE Trans Visual Comput Gr 22(1):698–707
Sharma G, Wu W, Dalal EN (2005) The ciede2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30
Szafir DA (2017) Modeling color difference for visualization design. IEEE Trans Visual Comput Gr 24(1):392–401
Ullmann JR (1976) An algorithm for subgraph isomorphism. J ACM (JACM) 23(1):31–42
Ullmann JR (2010) Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism. J Exp Algorithmics (JEA) 15:1–6
Valdez P, Mehrabian A (1994) Effects of color on emotions. J Exp Psychol Gen 123(4):394
Vento M, Jiang X, Foggia P (2015) International contest on pattern search in biological databases
Wang Y, Chen X, Ge T, Bao C, Sedlmair M, Fu CW, Deussen O, Chen B (2018) Optimizing color assignment for perception of class separability in multiclass scatterplots. IEEE Trans Visual Comput Gr 25(1):820–829
Yang Y, Ming J, Yu N (2012) Color image quality assessment based on ciede2000. Adv Multimedia 2012(11):99
Yuan L, Zhou Z, Zhao J, Guo Y, Du F, Qu H (2021) Infocolorizer: interactive recommendation of color palettes for infographics. IEEE Trans Visual Comput Gr 5:1–1
Zeng Q, Zhao Y, Wang Y, Zhang J, Cao Y, Tu C, Viola I, Wang Y (2021) http://hdl.handle.net/10754/670899
Acknowledgments
The authors wish to acknowledge the support from NSFC under Grants (No. 62072183 and 62102278), Major Program of National Social Science Foundation of China under Grant (No. 22ZD05) and the Shanghai Committee of Science and Technology, China (Grant No. 22511104600).
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Li, K., Li, J., Sun, Y. et al. Color assignment optimization for categorical data visualization with adjacent blocks. J Vis 26, 917–936 (2023). https://doi.org/10.1007/s12650-022-00905-z
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DOI: https://doi.org/10.1007/s12650-022-00905-z