Computational models of visual saliency have been used to detect salient regions and simulate human eye-gaze on images and videos.

A majority of the existing approaches are highly parametric in nature.

They are specialized to predict either eye-gaze or detect salient

regions, but not both simultaneously. Like other computer vision approaches,

the saliency models too impose pre-specified grids to process

the image. In this context we explore ways of exploiting random/

stochastic algorithmic approaches for saliency computation to

address issues like pre-specified grids, computational efficiency, parameter

set etc. We propose three different approaches for saliency

computation on images and provide elaborate benchmarking results

with respect to other saliency systems. Consequently, we have been

successful in improving the state-of-the-art in terms of eye-gaze prediction

and salient region detection performance of the saliency systems.

In addition, we have extended one of our proposed saliency

approaches to predict eye-gaze while viewing a tutoring or goaldirected

action scenario. Along with the proposed algorithms, we

also have created a video dataset for evaluating saliency systems in

the context of goal-directed action. We hope that the proposed approaches

for saliency computation, exprimental protocols, resulting

video dataset and the ensuing discussions will help the community

in developing more sophisticated systems of visual saliency.