VisImages: A Fine-Grained Expert-Annotated Visualization Dataset

Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization....

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Bibliographic Details
Published inarXiv.org
Main Authors Deng, Dazhen, Wu, Yihong, Shu, Xinhuan, Wu, Jiang, Fu, Siwei, Cui, Weiwei, Wu, Yingcai
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.03.2022
Subjects
Annotations
Boxes
Computer Science - Computer Vision and Pattern Recognition
Documents
Empirical analysis
Image detection
Image quality
Machine learning
Quality control
Representations
Spatial distribution
Taxonomy
Visualization
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Summary:Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built upon a comprehensive visualization taxonomy, the dataset includes 35,096 visualizations and their bounding boxes in the images.We demonstrate the usefulness of VisImages through three use cases: 1) investigating the use of visualizations in the publications with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing visualizations in the visual analytics systems automatically.
ISSN:2331-8422
DOI:10.48550/arxiv.2007.04584