Cited By
View all- Chen JYang WJia ZXiao LLiu S(2024)Dynamic Color Assignment for Hierarchical DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345638631:1(338-348)Online publication date: 9-Sep-2024
Color assignment optimization for categorical data visualization with adjacent blocks
Pages 917 - 936
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.
Graphical abstract
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References
[1]
Arvind V, Das B, Köbler J, and Toda S Colored hypergraph isomorphism is fixed parameter tractable Algorithmica 2015 71 1 120-138
[2]
Bonnici V, Giugno R, Pulvirenti A, Shasha D, and Ferro A A subgraph isomorphism algorithm and its application to biochemical data BMC Bioinform 2013 14 7 S13
[3]
Cheng S, Xu W, and Mueller K Colormap nd: a data-driven approach and tool for mapping multivariate data to color IEEE Trans Visual Comput Gr 2018 25 2 1361-1377
[4]
Colbourn CJ On testing isomorphism of permutation graphs Networks 1981 11 1 13-21
[5]
Cook SA (1971) The complexity of theorem-proving procedures. In: The third annual ACM symposium on theory of computing, pp 151–158
[6]
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
[7]
Cordella LP, Foggia P, Sansone C, and Vento M A (sub) graph isomorphism algorithm for matching large graphs IEEE Trans Pattern Anal Mach Intell 2004 26 10 1367-1372
[8]
Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml
[9]
Fang H, Walton S, Delahaye E, Harris J, Storchak D, and Chen M Categorical colormap optimization with visualization case studies IEEE Trans Visual Comput Gr 2016 23 1 871-880
[10]
Gramazio CC, Laidlaw DH, and Schloss KB Colorgorical: creating discriminable and preferable color palettes for information visualization IEEE Trans Visual Comput Gr 2017 23 1 521-530
[11]
Holy T (2011) Generate maximally perceptually-distinct colors. https://ww2.mathworks.cn/matlabcentral/fileexchange/29702-generate-maximally-perceptually-distinct-colors
[12]
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
[13]
Huertas R, Melgosa M, and Hita E Influence of random-dot textures on perception of suprathreshold color differences J Opt Soc Am A 2006 23 9 2067-2076
[14]
Kim HR, Yoo MJ, Kang H, and Lee IK Perceptually-based color assignment Comput Gr Forum 2014 33 309-318
[15]
Lee S, Sips M, and Seidel HP Perceptually driven visibility optimization for categorical data visualization IEEE Trans Visual Comput Gr 2012 19 10 1746-1757
[16]
Li D, Mei H, Shen Y, Su S, Zhang W, Wang J, Zu M, and Chen W Echarts: a declarative framework for rapid construction of web-based visualization Visual Inform 2018 2 2 136-146
[17]
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
[18]
Lin S, Fortuna J, Kulkarni C, Stone M, and Heer J Selecting semantically-resonant colors for data visualization Comput Gr Forum 2013 32 401-410
[19]
Liu S and Pei M Texture-aware emotional color transfer between images IEEE Access 2018 6 31375-31386
[20]
Lu K, Feng M, Chen X, Sedlmair M, Deussen O, Lischinski D, Cheng Z, and Wang Y Palettailor: discriminable colorization for categorical data IEEE Trans Visual Comput Gr 2020 27 2 475-484
[21]
Lueker GS and Booth KS A linear time algorithm for deciding interval graph isomorphism J ACM (JACM) 1979 26 2 183-195
[22]
Luks EM Isomorphism of graphs of bounded valence can be tested in polynomial time J Comput Syst Sci 1982 25 1 42-65
[23]
McKay BD Computing automorphisms and canonical labellings of graphs Combin Math 1978 2 223-232
[24]
McKay BD et al. (1981) Practical graph isomorphism. Vanderbilt University Tennessee, USA, Department of Computer Science
[25]
Mokrzycki W and Tatol M Color difference delta e-a survey Mach Gr Vis 2011 20 383-411
[26]
Othman A, Wook TSMT, and Qamar F Categorizing color appearances of image scenes based on human color perception for image retrieval IEEE Access 2020 8 161692-161701
[27]
Ou LC and Luo MR A colour harmony model for two-colour combinations Color Res Appl 2006 31 3 191-204
[28]
Setlur V and Stone MC A linguistic approach to categorical color assignment for data visualization IEEE Trans Visual Comput Gr 2015 22 1 698-707
[29]
Sharma G, Wu W, and Dalal EN The ciede2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations Color Res Appl 2005 30 1 21-30
[30]
Szafir DA Modeling color difference for visualization design IEEE Trans Visual Comput Gr 2017 24 1 392-401
[31]
Ullmann JR An algorithm for subgraph isomorphism J ACM (JACM) 1976 23 1 31-42
[32]
Ullmann JR Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism J Exp Algorithmics (JEA) 2010 15 1-6
[33]
Valdez P and Mehrabian A Effects of color on emotions J Exp Psychol Gen 1994 123 4 394
[34]
Vento M, Jiang X, Foggia P (2015) International contest on pattern search in biological databases
[35]
Wang Y, Chen X, Ge T, Bao C, Sedlmair M, Fu CW, Deussen O, and Chen B Optimizing color assignment for perception of class separability in multiclass scatterplots IEEE Trans Visual Comput Gr 2018 25 1 820-829
[36]
Yang Y, Ming J, and Yu N Color image quality assessment based on ciede2000 Adv Multimedia 2012 2012 11 99
[37]
Yuan L, Zhou Z, Zhao J, Guo Y, Du F, and Qu H Infocolorizer: interactive recommendation of color palettes for infographics IEEE Trans Visual Comput Gr 2021 5 1-1
[38]
Zeng Q, Zhao Y, Wang Y, Zhang J, Cao Y, Tu C, Viola I, Wang Y (2021) http://hdl.handle.net/10754/670899
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© The Visualization Society of Japan 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
Published: 06 January 2023
Accepted: 05 December 2022
Revision received: 15 September 2022
Received: 22 June 2022
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- Major Program of National Social Science Foundation of China
- National Natural Science Foundation of China
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Cited By
View all- Chen JYang WJia ZXiao LLiu S(2024)Dynamic Color Assignment for Hierarchical DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345638631:1(338-348)Online publication date: 9-Sep-2024