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

2025-11-14

AI Summary

Existing multiview visualization techniques rely heavily on explicit visual links and interactions, overlooking the potential of color to implicitly encode data relationships. To address this, we propose C2Views—a knowledge graph–based framework for consistent color mapping across multiview visualizations. Our approach models multiview components and their semantic relationships as a knowledge graph and formalizes color assignment as a Pareto optimization problem balancing intra-view discriminability and inter-view consistency. We integrate a genetic algorithm with an interactive colormap design interface to enable human-in-the-loop optimization. Evaluation demonstrates that C2Views significantly outperforms baseline methods in both color discriminability and cross-view consistency, effectively reducing cognitive load and simplifying exploratory analysis of complex multiview data.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multiple-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.

Problem

• Designing colormaps for consistent data encoding across multiple visualization views
• Balancing single-view effectiveness with multi-view consistency through optimization
• Reducing graphical complexity while maintaining color distinction across views

Innovation

• Knowledge-based graph specification for colormap relationships
• Genetic algorithm with Pareto optimality for optimization
• Interactive interface enabling user-intended colormap refinement