C2Views: Knowledge-based Colormap Design for Multiple-View Consistency

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dc.contributor.authorHou, Yihanen_US
dc.contributor.authorYe, Yilinen_US
dc.contributor.authorWang, Liangweien_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.authorZeng, Weien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:04:58Z
dc.date.available2025-10-07T06:04:58Z
dc.date.issued2025
dc.description.abstractMultiple-view (MV) visualization provides a comprehensive and integrated perspective on complex data, establishing itself as an effective method for visual communication and exploratory data analysis. While existing studies have predominantly focused on designing explicit visual linkages and coordinated interactions to facilitate the exploration of MV visualizations, these approaches often demand extra graphical and interactive effort, overlooking the potential of color as an effective channel for encoding data and relationships. Addressing this oversight, we introduce C2Views, a new framework for colormap design that implicitly shows the relation across views. We begin by structuring the components and their relationships within MVs into a knowledge-based graph specification, wherein colormaps, data, and views are denoted as entities, and the interactions among them are illustrated as relations. Building on this representation, we formulate the design criteria as an optimization problem and employ a genetic algorithm enhanced by Pareto optimality, generating colormaps that balance single-view effectiveness and multiple-view consistency. Our approach is further complemented with an interactive interface for user-intended refinement. We demonstrate the feasibility of C2Views through various colormap design examples for MVs, underscoring its adaptability to diverse data relationships and view layouts. Comparative user studies indicate that our method outperforms the existing approach in facilitating color distinction and enhancing multiple-view consistency, thereby simplifying data exploration processes.en_US
dc.description.sectionheadersVisualization
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251301
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251301
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251301
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization systems and tools
dc.subjectHuman centered computing → Visualization systems and tools
dc.titleC2Views: Knowledge-based Colormap Design for Multiple-View Consistencyen_US
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