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

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dc.contributor.author Fernández Martínez, Fernando
dc.contributor.author Hernández García, Alejandro
dc.contributor.author Fernández Torres, Miguel Ángel
dc.contributor.author González Díaz, Iván
dc.contributor.author Garcia Faura, Alvaro
dc.contributor.author Díaz de María, Fernando
dc.date.accessioned 2020-09-16T09:29:08Z
dc.date.available 2020-09-16T09:29:08Z
dc.date.issued 2018-08-01
dc.identifier.bibliographicCitation Multimedia Tools and Applications, (2018), 77(15), pp.: 18903–18933.
dc.identifier.issn 1380-7501
dc.identifier.uri http://hdl.handle.net/10016/30820
dc.description.abstract Content 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.
dc.description.sponsorship This 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.
dc.format.extent 30
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer Science+Business Media, LLC 2017
dc.subject.other Visual attention
dc.subject.other Saliency
dc.subject.other Scene analysis
dc.subject.other Aesthetics assessment
dc.subject.other Feature extraction
dc.subject.other Video impact assessment
dc.title Exploiting visual saliency for assessing the impact of car commercials upon viewers
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1007/s11042-017-5339-9
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2014-53390-P
dc.relation.projectID Gobierno de España. RTC-2016-5305-7
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 18903
dc.identifier.publicationissue 15
dc.identifier.publicationlastpage 18933
dc.identifier.publicationtitle Multimedia Tools and Applications
dc.identifier.publicationvolume 77
dc.identifier.uxxi AR/0000021924
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