Data-driven Evaluation of Visual Quality Measures

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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
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@article{
10.1111:cgf.12632
, journal = {Computer Graphics Forum}, title = {{
Data-driven Evaluation of Visual Quality Measures
}}, author = {
Sedlmair, Michael
and
Aupetit, Michael
}, year = {
2015
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, DOI = {
10.1111/cgf.12632
} }
Citation