InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements? spatial arrangement. We propose a data-driven method that provides flexibility by considering users? preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.

[1]  Frédo Durand,et al.  Understanding Infographics through Textual and Visual Tag Prediction , 2017, ArXiv.

[2]  John T. Stasko,et al.  Data Illustrator: Augmenting Vector Design Tools with Lazy Data Binding for Expressive Visualization Authoring , 2018, CHI.

[3]  Karen B. Schloss,et al.  Visual aesthetics and human preference. , 2013, Annual review of psychology.

[4]  Mira Dontcheva,et al.  Data-Driven Guides: Supporting Expressive Design for Information Graphics , 2017, IEEE Transactions on Visualization and Computer Graphics.

[5]  Çagatay Demiralp,et al.  Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks , 2018, IEEE Computer Graphics and Applications.

[6]  Mihaela van der Schaar,et al.  GAIN: Missing Data Imputation using Generative Adversarial Nets , 2018, ICML.

[7]  Christopher A. Brooks,et al.  Useful junk?: the effects of visual embellishment on comprehension and memorability of charts , 2010, CHI.

[8]  Guoliang Li,et al.  DeepEye: Towards Automatic Data Visualization , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[9]  Arvind Satyanarayan,et al.  Lyra: An Interactive Visualization Design Environment , 2014, Comput. Graph. Forum.

[10]  Clayton D. Scott,et al.  Robust kernel density estimation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Danielle Albers Szafir,et al.  Color Crafting: Automating the Construction of Designer Quality Color Ramps , 2019, IEEE Transactions on Visualization and Computer Graphics.

[12]  Kanit Wongsuphasawat,et al.  Towards a general-purpose query language for visualization recommendation , 2016, HILDA '16.

[13]  ColorBrewer , 2021 .

[14]  Kenneth Moreland,et al.  Diverging Color Maps for Scientific Visualization , 2009, ISVC.

[15]  Xi Chen,et al.  InfoNice: Easy Creation of Information Graphics , 2018, CHI.

[16]  Krzysztof Z Gajos,et al.  BubbleView , 2017, ACM Trans. Comput. Hum. Interact..

[17]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[18]  Yun Wang,et al.  Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements , 2019, IEEE Transactions on Visualization and Computer Graphics.

[19]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[20]  Rynson W. H. Lau,et al.  What characterizes personalities of graphic designs? , 2018, ACM Trans. Graph..

[21]  Aaron Hertzmann,et al.  Color compatibility from large datasets , 2011, ACM Trans. Graph..

[22]  Danielle Albers Szafir,et al.  Modeling Color Difference for Visualization Design , 2018, IEEE Transactions on Visualization and Computer Graphics.

[23]  Celine Latulipe,et al.  Quantifying the Creativity Support of Digital Tools through the Creativity Support Index , 2014, ACM Trans. Comput. Hum. Interact..

[24]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[25]  Wencheng Wu,et al.  The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations , 2005 .

[26]  Yun Wang,et al.  DataShot: Automatic Generation of Fact Sheets from Tabular Data , 2020, IEEE Transactions on Visualization and Computer Graphics.

[27]  Anne Morgan Spalter,et al.  Interactive color palette tools , 2004, IEEE Computer Graphics and Applications.

[28]  Cynthia A. Brewer,et al.  ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps , 2003 .

[29]  Heidrun Schumann,et al.  Task-Driven Color Coding , 2008, 2008 12th International Conference Information Visualisation.

[30]  Dani Lischinski,et al.  Palettailor: Discriminable Colorization for Categorical Data , 2020, IEEE Transactions on Visualization and Computer Graphics.

[31]  Jeffrey Heer,et al.  Color naming models for color selection, image editing and palette design , 2012, CHI.

[32]  Dmitry Vetrov,et al.  Variational Autoencoder with Arbitrary Conditioning , 2018, ICLR.

[33]  Thomas Ertl,et al.  PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning , 2020, IEEE Transactions on Visualization and Computer Graphics.

[34]  Vidya Setlur,et al.  A Linguistic Approach to Categorical Color Assignment for Data Visualization , 2016, IEEE Transactions on Visualization and Computer Graphics.

[35]  Daniel Cohen-Or,et al.  Exploring Visual Information Flows in Infographics , 2020, CHI.

[36]  Yingcai Wu,et al.  What Makes a Data-GIF Understandable? , 2020, IEEE Transactions on Visualization and Computer Graphics.

[37]  E. Feichtner,et al.  On the topology of nested set complexes , 2003, math/0311430.

[38]  Hans-Peter Seidel,et al.  Perceptually Driven Visibility Optimization for Categorical Data Visualization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[39]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[40]  Daniel A. Keim,et al.  Methods for Compensating Contrast Effects in Information Visualization , 2014, Comput. Graph. Forum.

[41]  Michael S. Brown,et al.  Group‐Theme Recoloring for Multi‐Image Color Consistency , 2017, Comput. Graph. Forum.

[42]  Steven Franconeri,et al.  ISOTYPE Visualization: Working Memory, Performance, and Engagement with Pictographs , 2015, CHI.

[43]  Frédo Durand,et al.  Learning Visual Importance for Graphic Designs and Data Visualizations , 2017, UIST.

[44]  Maureen C. Stone,et al.  Affective Color in Visualization , 2017, CHI.

[45]  Stef van Buuren,et al.  MICE: Multivariate Imputation by Chained Equations in R , 2011 .

[46]  Kanit Wongsuphasawat,et al.  Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations , 2016, IEEE Transactions on Visualization and Computer Graphics.

[47]  Sanja Fidler,et al.  Color Builder: A Direct Manipulation Interface for Versatile Color Theme Authoring , 2019, CHI.

[48]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Wendy E. Mackay,et al.  Color Portraits: From Color Picking to Interacting with Color , 2015, CHI.

[50]  Katharina Reinecke,et al.  Infographic Aesthetics: Designing for the First Impression , 2015, CHI.

[51]  Weiwei Cui,et al.  Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison , 2021, CHI.

[52]  Daniel J. Wigdor,et al.  DataInk: Direct and Creative Data-Oriented Drawing , 2018, CHI.

[53]  Babak Saleh,et al.  Learning style similarity for searching infographics , 2015, Graphics Interface.

[54]  Jeffrey Heer,et al.  Selecting Semantically‐Resonant Colors for Data Visualization , 2013, Comput. Graph. Forum.

[55]  Alain Trémeau,et al.  A region growing and merging algorithm to color segmentation , 1997, Pattern Recognit..

[56]  David H. Laidlaw,et al.  Colorgorical: Creating discriminable and preferable color palettes for information visualization , 2017, IEEE Transactions on Visualization and Computer Graphics.

[57]  Jeffrey Heer,et al.  Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco , 2018, IEEE Transactions on Visualization and Computer Graphics.

[58]  Zening Qu,et al.  Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring , 2019, IEEE Transactions on Visualization and Computer Graphics.

[59]  Yong Wang,et al.  Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline , 2019, IEEE Transactions on Visualization and Computer Graphics.

[60]  Daniel Cohen-Or,et al.  Color harmonization , 2006, ACM Trans. Graph..

[61]  Chi-Wing Fu,et al.  Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots , 2019, IEEE Transactions on Visualization and Computer Graphics.

[62]  Tim Kraska,et al.  VizML: A Machine Learning Approach to Visualization Recommendation , 2018, CHI.

[63]  Edwin de Jonge,et al.  Tree Colors: Color Schemes for Tree-Structured Data , 2014, IEEE Transactions on Visualization and Computer Graphics.

[64]  Kanit Wongsuphasawat,et al.  Voyager 2: Augmenting Visual Analysis with Partial View Specifications , 2017, CHI.

[65]  Haim Levkowitz,et al.  Color scales for image data , 1992, IEEE Computer Graphics and Applications.

[66]  Hanspeter Pfister,et al.  What Makes a Visualization Memorable? , 2013, IEEE Transactions on Visualization and Computer Graphics.