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Spatiotemporal Features for Action Recognition and Salient Event Detection

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dc.contributor.author Rapantzikos, K en
dc.contributor.author Avrithis, Y en
dc.contributor.author Kollias, S en
dc.date.accessioned 2014-03-01T01:37:06Z
dc.date.available 2014-03-01T01:37:06Z
dc.date.issued 2011 en
dc.identifier.issn 1866-9956 en
dc.identifier.uri http://hdl.handle.net/123456789/21448
dc.subject Action recognition en
dc.subject Salient event detection en
dc.subject Spatiotemporal visual saliency en
dc.subject Volumetric representation en
dc.subject.other Action recognition en
dc.subject.other Computational model en
dc.subject.other Constrained minimization en
dc.subject.other Data sets en
dc.subject.other Event detection en
dc.subject.other Event detection in video en
dc.subject.other Gestalt law en
dc.subject.other Human vision en
dc.subject.other Human visual en
dc.subject.other Multiple resolutions en
dc.subject.other Novel methods en
dc.subject.other Salient event detection en
dc.subject.other Spatio temporal features en
dc.subject.other Spatiotemporal regions en
dc.subject.other Temporal segments en
dc.subject.other Video sequences en
dc.subject.other Visual feature en
dc.subject.other Visual saliency en
dc.subject.other Volumetric representation en
dc.subject.other Competition en
dc.subject.other Object recognition en
dc.subject.other Video recording en
dc.subject.other Visualization en
dc.subject.other Feature extraction en
dc.title Spatiotemporal Features for Action Recognition and Salient Event Detection en
heal.type journalArticle en
heal.identifier.primary 10.1007/s12559-011-9097-0 en
heal.identifier.secondary http://dx.doi.org/10.1007/s12559-011-9097-0 en
heal.language English en
heal.publicationDate 2011 en
heal.abstract Although the mechanisms of human visual understanding remain partially unclear, computational models inspired by existing knowledge on human vision have emerged and applied to several fields. In this paper, we propose a novel method to compute visual saliency from video sequences by counting in the actual spatiotemporal nature of the video. The visual input is represented by a volume in space-time and decomposed into a set of feature volumes in multiple resolutions. Feature competition is used to produce a saliency distribution of the input implemented by constrained minimization. The proposed constraints are inspired by and associated with the Gestalt laws. There are a number of contributions in this approach, namely extending existing visual feature models to a volumetric representation, allowing competition across features, scales and voxels, and formulating constraints in accordance with perceptual principles. The resulting saliency volume is used to detect prominent spatiotemporal regions and consequently applied to action recognition and perceptually salient event detection in video sequences. Comparisons against established methods on public datasets are given and reveal the potential of the proposed model. The experiments include three action recognition scenarios and salient temporal segment detection in a movie database annotated by humans. © 2011 Springer Science+Business Media, LLC. en
heal.publisher SPRINGER en
heal.journalName Cognitive Computation en
dc.identifier.doi 10.1007/s12559-011-9097-0 en
dc.identifier.isi ISI:000292777700014 en
dc.identifier.volume 3 en
dc.identifier.issue 1 en
dc.identifier.spage 167 en
dc.identifier.epage 184 en


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