Publication:
Exploiting visual saliency for assessing the impact of car commercials upon viewers

dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorFernández Martínez, Fernando
dc.contributor.authorHernández García, Alejandro
dc.contributor.authorFernández Torres, Miguel Ángel
dc.contributor.authorGonzález Díaz, Iván
dc.contributor.authorGarcia Faura, Alvaro
dc.contributor.authorDíaz de María, Fernando
dc.date.accessioned2020-09-16T09:29:08Z
dc.date.available2020-09-16T09:29:08Z
dc.date.issued2018-08-01
dc.description.abstractContent based video indexing and retrieval (CBVIR) is a lively area of research which focuses on automating the indexing, retrieval and management of videos. This area has a wide spectrum of promising applications where assessing the impact of audiovisual productions emerges as a particularly interesting and motivating one. In this paper we present a computational model capable to predict the impact (i.e. positive or negative) upon viewers of car advertisements videos by using a set of visual saliency descriptors. Visual saliency provides information about parts of the image perceived as most important, which are instinctively targeted by humans when looking at a picture or watching a video. For this reason we propose to exploit visual information, introducing it as a new feature which reflects high-level semantics objectively, to improve the video impact categorization results. The suggested salience descriptors are inspired by the mechanisms that underlie the attentional abilities of the human visual system and organized into seven distinct families according to different measurements over the identified salient areas in the video frames, namely population, size, location, geometry, orientation, movement and photographic composition. Proposed approach starts by computing saliency maps for all the video frames, where two different visual saliency detection frameworks have been considered and evaluated: the popular graph based visual saliency (GBVS) algorithm, and a state-of-the-art DNN-based approach.en
dc.description.sponsorshipThis work has been partially supported by the National Grants RTC-2016-5305-7 and TEC2014-53390-P of the Spanish Ministry of Economy and Competitiveness.en
dc.description.statusPublicadoes
dc.format.extent30
dc.identifier.bibliographicCitationMultimedia Tools and Applications, (2018), 77(15), pp.: 18903–18933.en
dc.identifier.doihttps://doi.org/10.1007/s11042-017-5339-9
dc.identifier.issn1380-7501
dc.identifier.publicationfirstpage18903
dc.identifier.publicationissue15
dc.identifier.publicationlastpage18933
dc.identifier.publicationtitleMultimedia Tools and Applicationsen
dc.identifier.publicationvolume77
dc.identifier.urihttps://hdl.handle.net/10016/30820
dc.identifier.uxxiAR/0000021924
dc.language.isoengen
dc.publisherSpringer
dc.relation.projectIDGobierno de España. TEC2014-53390-Pes
dc.relation.projectIDGobierno de España. RTC-2016-5305-7es
dc.rights© Springer Science+Business Media, LLC 2017en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherVisual attentionen
dc.subject.otherSaliencyen
dc.subject.otherScene analysisen
dc.subject.otherAesthetics assessmenten
dc.subject.otherFeature extractionen
dc.subject.otherVideo impact assessmenten
dc.titleExploiting visual saliency for assessing the impact of car commercials upon viewersen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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