Understanding and predicting where people look in images

Abstract

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. This is a challenging task given that no one fully understands how the human visual system works. This thesis explores the way people look at different types of images and provides methods of predicting where they look in new scenes. We describe a new way to model where people look from ground truth eye tracking data using techniques of machine learning that outperforms all existing models, and provide a benchmark data set to quantitatively compare existing and future models. In addition we explore how image resolution affects where people look. Our experiments, models, and large eye tracking data sets should help future researchers better understand and predict where people look in order to create more powerful computational vision systems.