ReVision Corpus

vis16cat.txt : 3201 URLs in 16 categories A tab-delimited file with each line giving the category and then the URL of an image. vis10cat.txt : 2223 URLs in 10 categories Subset of 16 category corpus corresponding to categories used in the paper. The evaluation in the paper incorporates the bar, line, pie and scatter categories from the corpus of "Classifying Computer Generated Charts" by [Prasad et al. 2007] and collapses the bar and column categories of the 16 category corpus into a single bar category. Image de-duplication was performed after merging with Prasad et al.'s corpus to remove duplicates.

Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves an image classification accuracy of 96% across ten chart categories. It also accurately extracts marks from 79% of bar charts and 62% of pie charts, and from these charts it successfully extracts the data from 71% of bar charts and 64% of pie charts. ReVision then applies perceptually-based design…