On computational modeling of visual saliency: Examining what's right, and what's left.

In the past decade, a large number of computational models of visual saliency have been proposed. Recently a number of comprehensive benchmark studies have been presented, with the goal of assessing the performance landscape of saliency models under varying conditions. This has been accomplished by considering fixation data, annotated image regions, and stimulus patterns inspired by psychophysics. In this paper, we present a high-level examination of challenges in computational modeling of visual saliency, with a heavy emphasis on human vision and neural computation. This includes careful assessment of different metrics for performance of visual saliency models, and identification of remaining difficulties in assessing model performance. We also consider the importance of a number of issues relevant to all saliency models including scale-space, the impact of border effects, and spatial or central bias. Additionally, we consider the biological plausibility of models in stepping away from exemplar input patterns towards a set of more general theoretical principles consistent with behavioral experiments. As a whole, this presentation establishes important obstacles that remain in visual saliency modeling, in addition to identifying a number of important avenues for further investigation.