Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison
arXiv:2101.03680
Abstract
We contribute a method to automate parameter configurations for chart layouts by learning from human preferences. Existing charting tools usually determine the layout parameters using predefined heuristics, producing sub-optimal layouts. People can repeatedly adjust multiple parameters (e.g., chart size, gap) to achieve visually appealing layouts. However, this trial-and-error process is unsystematic and time-consuming, without a guarantee of improvement. To address this issue, we develop Layout Quality Quantifier (LQ2), a machine learning model that learns to score chart layouts from pairwise crowdsourcing data. Combined with optimization techniques, LQ2 recommends layout parameters that improve the charts' layout quality. We apply LQ2 on bar charts and conduct user studies to evaluate its effectiveness by examining the quality of layouts it produces. Results show that LQ2 can generate more visually appealing layouts than both laypeople and baselines. This work demonstrates the feasibility and usages of quantifying human preferences and aesthetics for chart layouts.
arXiv commentAccepted at ACM CHI 2021
References in corpus (5)
- Calliope: Automatic Visual Data Story Generation from a Spreadsheet
- Learning Visual Importance for Graphic Designs and Data Visualizations
- Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline
- Augmenting Static Visualizations with PapARVis Designer
- Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach