Spatial Bayesian surprise for image saliency and quality assessment

Gkioulekas, I; Evangelopoulos, G; Maragos, P

dc.contributor.author	Gkioulekas, I	en
dc.contributor.author	Evangelopoulos, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:47:00Z
dc.date.available	2014-03-01T02:47:00Z
dc.date.issued	2010	en
dc.identifier.issn	15224880	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32981
dc.subject	Bayesian surprise	en
dc.subject	Image quality assessment	en
dc.subject	Image saliency	en
dc.subject	Region detection	en
dc.subject	Visual attention	en
dc.subject.other	Bayesian	en
dc.subject.other	Image quality assessment	en
dc.subject.other	Image saliency	en
dc.subject.other	Region detection	en
dc.subject.other	Visual Attention	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Image quality	en
dc.subject.other	Imaging systems	en
dc.subject.other	Rating	en
dc.subject.other	Tracking (position)	en
dc.title	Spatial Bayesian surprise for image saliency and quality assessment	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2010.5650991	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2010.5650991	en
heal.identifier.secondary	5650991	en
heal.publicationDate	2010	en
heal.abstract	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. © 2010 IEEE.	en
heal.journalName	Proceedings - International Conference on Image Processing, ICIP	en
dc.identifier.doi	10.1109/ICIP.2010.5650991	en
dc.identifier.spage	1081	en
dc.identifier.epage	1084	en