  Humans' ability to detect and locate salient objects on images is remarkably
fast and successful. Performing this process by using eye tracking equipment is
expensive and cannot be easily applied, and computer modeling of this human
behavior is still a problem to be solved. In our study, one of the largest
public eye-tracking databases which has fixation points of 15 observers on 1003
images is used. In addition to low, medium and high-level features which have
been used in previous studies, SIFT features extracted from the images are used
to improve the classification accuracy of the models. A second contribution of
this paper is the comparison and statistical analysis of different machine
learning methods that can be used to train our model. As a result, a best
feature set and learning model to predict where humans look at images, is
determined.
