Spatial bayesian surprise for image saliency and quality assessment
We propose an alternative interpretation of Bayesian surprise in the spatial domain, to account for saliency arising from contrast in image context. Our saliency formulation is integrated in three different application scenaria, with considerable improvements in performance: 1) visual attention prediction, validated using eye- and mouse-tracking data, 2) region of interest detection, to improve scale selection and localization, 3) image quality assessment to achieve better agreement with subjective human evaluations.