  In this paper we propose a Kalman filter aided saliency detection model which
is based on the conjecture that salient regions are considerably different from
our "visual expectation" or they are "visually surprising" in nature. In this
work, we have structured our model with an immediate objective to predict
saliency in static images. However, the proposed model can be easily extended
for space-time saliency prediction. Our approach was evaluated using two
publicly available benchmark data sets and results have been compared with
other existing saliency models. The results clearly illustrate the superior
performance of the proposed model over other approaches.
