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A new self-organizing neural gas model based on Bregman divergences
dc.contributor.author | Palomo-Ferrer, Esteban José | |
dc.contributor.author | Molina-Cabello, Miguel Ángel | |
dc.contributor.author | López-Rubio, Ezequiel | |
dc.contributor.author | Luque-Baena, Rafael Marcos | |
dc.date.accessioned | 2018-07-20T09:35:22Z | |
dc.date.available | 2018-07-20T09:35:22Z | |
dc.date.created | 2018 | |
dc.date.issued | 2018-07-20 | |
dc.identifier.uri | https://hdl.handle.net/10630/16315 | |
dc.description.abstract | In 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.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Lenguajes de ordenador - Congresos | en_US |
dc.subject.other | Self-organization | en_US |
dc.subject.other | Unsupervised learning | en_US |
dc.subject.other | Video processing | en_US |
dc.title | A new self-organizing neural gas model based on Bregman divergences | en_US |
dc.type | info:eu-repo/semantics/conferenceObject | en_US |
dc.centro | E.T.S.I. Informática | en_US |
dc.relation.eventtitle | 2018 International Joint Conference on Neural Networks (IJCNN) | en_US |
dc.relation.eventplace | Rio de Janeiro | en_US |
dc.relation.eventdate | 08/07/2018 | en_US |