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Probabilistic Topic Model for Context-Driven Visual Attention Understanding

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorFernández Torres, Miguel Ángel
dc.contributor.authorGonzález Díaz, Iván
dc.contributor.authorDíaz de María, Fernando
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-08-26T08:14:57Z
dc.date.available2020-08-26T08:14:57Z
dc.date.issued2019-04-04
dc.description.abstractModern computer vision techniques have to deal with vast amounts of visual data, which implies a computational effort that has often to be accomplished in broad and challenging scenarios. The interest in efficiently solving these image and video applications has led researchers to develop methods to expertly drive the corresponding processing to conspicuous regions that either depend on the context or are based on specific requirements. In this paper, we propose a general hierarchical probabilistic framework, independent of the application scenario, and relied on the most outstanding psychological studies about attention and eye movements which support that guidance is not based directly on the information provided by early visual processes but on a contextual representation that arose from them. The approach defines the task of context-driven visual attention as a mixture of latent sub-tasks, which are, in turn, modeled as a combination of specific distributions associated to low-, mid-, and high-level spatio-temporal features. Learning from fixations gathered from human observers, we incorporate an intermediate level between feature extraction and visual attention estimation that enables to obtain comprehensively guiding representations. The experiments show how our proposal successfully learns particularly adapted hierarchical explanations of visual attention in diverse video genres, outperforming several leading models in the literature.en
dc.description.sponsorshipThis work has been partially funded by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU13/04904, and the National Grants TEC2014-53390-P, TEC2014-61729-EXP and TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness.en
dc.description.statusPublicadoes
dc.format.extent14
dc.identifier.bibliographicCitationIEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(6), pp. 1653 - 1667.en
dc.identifier.doihttps://doi.org/10.1109/TCSVT.2019.2909427
dc.identifier.issn1051-8215
dc.identifier.publicationfirstpage1653
dc.identifier.publicationissue6
dc.identifier.publicationlastpage1667
dc.identifier.publicationtitleIEEE Transactions on Circuits and Systems for Video Technologyen
dc.identifier.publicationvolume30
dc.identifier.urihttp://hdl.handle.net/10016/30763
dc.identifier.uxxiAR/0000025406
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TEC2014-53390-Pes
dc.relation.projectIDGobierno de España. TEC2014-61729-EXPes
dc.relation.projectIDGobierno de España. TEC2017-84395-Pes
dc.rights© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherTop-down visual attentionen
dc.subject.otherHierarchical probabilistic frameworken
dc.subject.otherContext-aware modelen
dc.subject.otherLatent topic modelsen
dc.titleProbabilistic Topic Model for Context-Driven Visual Attention Understandingen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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