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ReVision: Automated Classification, Analysis and Redesign of Chart Images
 

Summary: ReVision: Automated Classification, Analysis
and Redesign of Chart Images
Manolis Savva, Nicholas Kong, Arti Chhajta, Li Fei-Fei, Maneesh Agrawala, Jeffrey Heer
Stanford University
{msavva, achhajta, feifeili, jheer}@cs.stanford.edu
University of California, Berkeley
{nkong, maneesh}@eecs.berkeley.edu
ABSTRACT
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 vi-
sual representations. In response we present ReVision, a sys-
tem that automatically redesigns visualizations to improve
graphical perception. Given a bitmap image of a chart as in-
put, 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 in-
fers the underlying data. Using a corpus of images drawn
from the web, ReVision achieves an image classification ac-

  

Source: Agrawala, Maneesh - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley
O'Brien, James F. - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley

 

Collections: Computer Technologies and Information Sciences