Spatial Bayesian surprise for image saliency and quality assessment
I Gkioulekas, G Evangelopoulos… - 2010 ieee international …, 2010 - ieeexplore.ieee.org
2010 ieee international conference on image processing, 2010•ieeexplore.ieee.org
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.
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.
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.
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