Bottom-Up Saliency Models for Still Images: A Practical Review

Abstract

There is an increasing interest to utilize human visual attention abilities on computational systems. This is especially the case for computer vision which needs to select the most relevant parts within a large amount of data. Therefore, modeling visual attention, particularly the bottom-up part, has been a very active research area over the past 20 years. Many different models of visual bottom-up attention are now available online. They take as input natural images and output a saliency map which gives the probability of each pixels to grab our attention. In this chapter, a state of the art of static saliency-based models has been performed. The models are grouped into different families depending if they predict human gaze or salient objects. Different features of those models are also provided to show the main differences between them.