VisImages: A Fine-Grained Expert-Annotated Visualization Dataset
This dataset addresses the need for fine-grained, expert-annotated visualization images to support the visualization community in tasks like literature analysis and model training, though it is incremental as it builds upon existing taxonomies.
The researchers tackled the lack of a systematic collection of images from visualization publications by creating VisImages, a dataset of 12,267 images from 1,397 papers with 35,096 annotated visualizations, and demonstrated its utility through three use cases including literature analysis and automated tasks.
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.