Paper Info
Title
Learning Visual Importance for Graphic Designs and Data Visualizations.
Abstract
Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.
Year
DOI
Venue
2017
10.1145/3126594.3126653
UIST '17: The 30th Annual ACM Symposium on User Interface Software and Technology Québec City QC Canada October, 2017
Keywords
DocType
Volume
Saliency, Computer Vision, Machine Learning, Eye Tracking, Visualization, Graphic Design, Deep Learning, Retargeting
Journal
abs/1708.02660
ISSN
ISBN
Citations 
UIST 2017
978-1-4503-4981-9
25
PageRank 
References 
Authors
0.68
30
9
Name
Order
Citations
PageRank
Zoya Gavrilov128716.20
Namwook Kim217912.31
Peter O'Donovan3281.75
Sami Alsheikh4271.66
Spandan Madan5282.05
Hanspeter Pfister65933340.59
Frédo Durand78625414.94
Bryan C. Russell82570217.78
Aaron Hertzmann96002352.67