Computer Graphics
TU Braunschweig

Learning a Perceptual Quality Metric for Correlation in Scatterplots

Learning a Perceptual Quality Metric for Correlation in Scatterplots

Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts’ expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15, 000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods.

Author(s):Leslie Wöhler, Yuxin Zou, Moritz Mühlhausen, Georgia Albuquerque, Marcus Magnor
Published:October 2019
Type:Article in conference proceedings
Book:Proc. Vision, Modeling and Visualization (VMV)
Presented at:Vision, Modeling and Visualization (VMV) 2019
Project(s): Scalable Visual Analytics 

  title = {Learning a Perceptual Quality Metric for Correlation in Scatterplots},
  author = {W{\"o}hler, Leslie and Zou, Yuxin and M{\"u}hlhausen, Moritz and Albuquerque, Georgia and Magnor, Marcus},
  booktitle = {Proc. Vision, Modeling and Visualization ({VMV})},
  organization = {Eurographics},
  month = {Oct},
  year = {2019}