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dc.contributor.authorPalomo, Esteban J.
dc.contributor.authorMolina-Cabello, Miguel A.
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorLuque-Baena, Rafael Marcos
dc.date.accessioned2018-07-20T09:35:22Z
dc.date.available2018-07-20T09:35:22Z
dc.date.created2018
dc.date.issued2018-07-20
dc.identifier.urihttps://hdl.handle.net/10630/16315
dc.description.abstractIn this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Techen_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLenguajes de ordenador - Congresosen_US
dc.subject.otherSelf-organizationen_US
dc.subject.otherUnsupervised learningen_US
dc.subject.otherVideo processingen_US
dc.titleA new self-organizing neural gas model based on Bregman divergencesen_US
dc.typeinfo:eu-repo/semantics/preprinten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle2018 International Joint Conference on Neural Networks (IJCNN)en_US
dc.relation.eventplaceRio de Janeiroen_US
dc.relation.eventdate08/07/2018en_US


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