  Visual attention refers to the human brain's ability to select relevant
sensory information for preferential processing, improving performance in
visual and cognitive tasks. It proceeds in two phases. One in which visual
feature maps are acquired and processed in parallel. Another where the
information from these maps is merged in order to select a single location to
be attended for further and more complex computations and reasoning. Its
computational description is challenging, especially if the temporal dynamics
of the process are taken into account. Numerous methods to estimate saliency
have been proposed in the last three decades. They achieve almost perfect
performance in estimating saliency at the pixel level, but the way they
generate shifts in visual attention fully depends on winner-take-all (WTA)
circuitry. WTA is implemented} by the biological hardware in order to select a
location with maximum saliency, towards which to direct overt attention. In
this paper we propose a gravitational model (GRAV) to describe the attentional
shifts. Every single feature acts as an attractor and {the shifts are the
result of the joint effects of the attractors. In the current framework, the
assumption of a single, centralized saliency map is no longer necessary, though
still plausible. Quantitative results on two large image datasets show that
this model predicts shifts more accurately than winner-take-all.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning