VisImages: A Fine-Grained Expert-Annotated Visualization Dataset

Dazhen Deng, Yihong Wu, Xinhuan Shu, Jiang Wu, Siwei Fu, Weiwei Cui, Yingcai Wu

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2023-10-24T22:36:00ZGMT-0600Change your timezone on the schedule page
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VisImages: A Fine-Grained Expert-Annotated Visualization Dataset
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Visualization dataset;crowdsourcing;literature analysis;visualization classification;visualization detection


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.