Data-driven Evaluation of Visual Quality Measures

Visual quality measures seek to algorithmically imitate human judgments of patterns such as class separability, correlation, or outliers. In this paper, we propose a novel data-driven framework for evaluating such measures. The basic idea is to take a large set of visually encoded data, such as scatterplots, with reliable human "ground truth" judgements, and to use this human-labeled data to learn how well a measure would predict human judgements on previously unseen data. Measures can then be evaluated based on predictive performance - an approach that is crucial for generalizing across datasets but has gained little attention so far. To illustrate our framework, we use it to evaluate 15 state-of-the-art class separation measures, using human ground truth data from 828 class separation judgments on color-coded 2D scatterplots.