Saliency Prediction for Mobile User Interfaces

Abstract

We introduce models for saliency prediction for mobile user interfaces. To enable performing a variety of tasks, a mobile interface may include elements like buttons, text, and links, in addition to natural images. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. In this work, we implement a system that collects eye-gaze data from mobile devices for free viewing task. Using the data collected in our experiments, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.