Learning Visual Importance for Graphic Designs and Data Visualizations

TL;DR

This paper introduces neural network models trained on human annotations to predict the importance of elements in visual designs, aiding automatic retargeting, thumbnailing, and interactive design feedback.

Contribution

The paper presents a new dataset and neural network models for predicting visual importance in designs, enabling improved automated and interactive design tools.

Findings

Models outperform existing saliency methods in importance prediction

Importance-driven applications match or surpass state-of-the-art performance

User studies validate effectiveness of importance-based design tools

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.