  This paper formulates bottom-up visual saliency as center surround
conditional entropy and presents a fast and efficient technique for the
computation of such a saliency map. It is shown that the new saliency
formulation is consistent with self-information based saliency,
decision-theoretic saliency and Bayesian definition of surprises but also faces
the same significant computational challenge of estimating probability density
in very high dimensional spaces with limited samples. We have developed a fast
and efficient nonparametric method to make the practical implementation of
these types of saliency maps possible. By aligning pixels from the center and
surround regions and treating their location coordinates as random variables,
we use a k-d partitioning method to efficiently estimating the center surround
conditional entropy. We present experimental results on two publicly available
eye tracking still image databases and show that the new technique is
competitive with state of the art bottom-up saliency computational methods. We
have also extended the technique to compute spatiotemporal visual saliency of
video and evaluate the bottom-up spatiotemporal saliency against eye tracking
data on a video taken onboard a moving vehicle with the driver's eye being
tracked by a head mounted eye-tracker.
