  We present a novel visual attention tracking technique based on Shared
Attention modeling. Our proposed method models the viewer as a participant in
the activity occurring in the scene. We go beyond image salience and instead of
only computing the power of an image region to pull attention to it, we also
consider the strength with which other regions of the image push attention to
the region in question. We use the term Attentional Push to refer to the power
of image regions to direct and manipulate the attention allocation of the
viewer. An attention model is presented that incorporates the Attentional Push
cues with standard image salience-based attention modeling algorithms to
improve the ability to predict where viewers will fixate. Experimental
evaluation validates significant improvements in predicting viewers' fixations
using the proposed methodology in both static and dynamic imagery.
