  By predicting where humans look in natural scenes, we can understand how they
perceive complex natural scenes and prioritize information for further
high-level visual processing. Several models have been proposed for this
purpose, yet there is a gap between best existing saliency models and human
performance. While many researchers have developed purely computational models
for fixation prediction, less attempts have been made to discover cognitive
factors that guide gaze. Here, we study the effect of a particular type of
scene structural information, known as the vanishing point, and show that human
gaze is attracted to the vanishing point regions. We record eye movements of 10
observers over 532 images, out of which 319 have vanishing points. We then
construct a combined model of traditional saliency and a vanishing point
channel and show that our model outperforms state of the art saliency models
using three scores on our dataset.

Computer Vision and Pattern Recognition