Learning to Predict Where People Look

Tilke Judd
Computer Science Artificial Intelligence Laboratory
MIT

For many applications in graphics, design, and human computer
interaction, it is essential to understand where humans look in a
scene.  Where eye tracking devices are not a viable option, models of
saliency can be used to predict fixation locations.  Most saliency
approaches are based on bottom-up computation that does not consider
top-down image semantics and often does not match actual eye
movements.  To address this problem, we collected a large database of
eye tracking data of 15 viewers on 1003 images and use this database
as training and testing examples to learn a model of saliency based on
low, middle and high-level image features.  We have made the eye tracking 
database available at http://people.csail.mit.edu/tjudd/wherepeoplelook.html.