Robustness and repeatability of saliency models subjected to visual degradations
The present study investigates the sensitivity of computationnal models of visual attention when subjected to visual degradations. One hundred and twenty natural color pictures were degraded using 6 filtering operations. By using different settings, five state-of-the-art models are used to compute 11400 saliency maps. The comparison of these maps to human saliency maps indicates that the tested models are robust to most of the visual degradations they were subjected to. These findings have implications on saliency-based applications, such as quality assessment and coding. A last point concerns the high repeatability of saliency models that might be used in a context of image retrieval.