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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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VisAnatomy: An SVG Chart Corpus with Fine-Grained Semantic Labels
IEEE Transactions on Visualization and Computer Graphics
|November 20, 2025
Summary
VISANATOMY is a new dataset of 942 charts with detailed labels for over 383,000 graphical elements. This resource enhances AI applications by providing fine-grained semantic information for data visualizations.
Area of Science:
- Computer Science
- Data Visualization
- Artificial Intelligence
Background:
- Existing chart corpora offer limited semantic labels, hindering AI-driven data visualization research.
- High-level labels in current datasets restrict their use in advanced AI applications.
Purpose of the Study:
- Introduce VISANATOMY, a novel corpus of real-world charts with multi-level, fine-grained semantic labels.
- Provide a comprehensive resource to advance research in data visualization and AI.
Main Methods:
- Collected 942 SVG charts from over 50 tools, covering 40 chart types.
- Annotated charts with detailed labels for over 383,000 graphical elements, including type, role, position, and visual encodings.
- Developed multi-level hierarchical groupings and layout information for chart components.
Main Results:
- VISANATOMY offers significantly richer semantic annotations compared to existing chart corpora.
- Demonstrated the corpus's utility through applications in semantic role inference, chart decomposition, classification, and accessibility.
Conclusions:
- VISANATOMY is a valuable resource for AI-powered data visualization research.
- The fine-grained labels enable new applications and deeper understanding of chart semantics.
- Further research can enhance the VISANATOMY corpus and its applications.
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