Bottom-up spatiotemporal visual attention model for video analysis

Rapantzikos, K; Tsapatsoulis, N; Avrithis, Y; Kollias, S

dc.contributor.author	Rapantzikos, K	en
dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Avrithis, Y	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:25:59Z
dc.date.available	2014-03-01T01:25:59Z
dc.date.issued	2007	en
dc.identifier.issn	1751-9659	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17858
dc.subject	Video Analysis	en
dc.subject	Visual Attention	en
dc.subject	Bottom Up	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computer vision	en
dc.subject.other	Content based retrieval	en
dc.subject.other	Mathematical models	en
dc.subject.other	Video signal processing	en
dc.subject.other	Computational visual attention (VA)	en
dc.subject.other	Human visual system (HVS)	en
dc.subject.other	Spatiotemporal visual attention	en
dc.subject.other	Video sequences	en
dc.subject.other	Image coding	en
dc.title	Bottom-up spatiotemporal visual attention model for video analysis	en
heal.type	journalArticle	en
heal.identifier.primary	10.1049/iet-ipr:20060040	en
heal.identifier.secondary	http://dx.doi.org/10.1049/iet-ipr:20060040	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown. © The Institution of Engineering and Technology 2007.	en
heal.publisher	INST ENGINEERING TECHNOLOGY-IET	en
heal.journalName	IET Image Processing	en
dc.identifier.doi	10.1049/iet-ipr:20060040	en
dc.identifier.isi	ISI:000249578200016	en
dc.identifier.volume	1	en
dc.identifier.issue	2	en
dc.identifier.spage	237	en
dc.identifier.epage	248	en