Learning Visual Importance for Graphic Designs and Data Visualizations
Name
1708.02660.pdf
Description
Accepted version
Size
8.29 MB
Format
Adobe PDF
Checksum (MD5)
58914750b442955b4af5d11214d41898
Author(s)
Bylinskii, Zoya
Kim, Nam Wook
O'Donovan, Peter
Alsheikh, Sami
Madan, Spandan
Pfister, Hanspeter
Durand, Fredo
Russell, Bryan
Hertzmann, Aaron
Date Issued
October 2017
Publisher
Association for Computing Machinery (ACM)
Citation
Bylinskii, Zoya, Kim, Nam Wook, O'Donovan, Peter, Alsheikh, Sami, Madan, Spandan et al. 2017. "Learning Visual Importance for Graphic Designs and Data Visualizations."
Version
Author's final manuscript
Abstract
© 2017 ACM. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3126594.3126653
