2026-06-02T14:38:48Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/163152026-02-03T11:51:18Zcom_10630_2254col_10630_37959

Palomo-Ferrer, Esteban José Molina-Cabello, Miguel Ángel López-Rubio, Ezequiel Luque-Baena, Rafael Marcos 2018-07-20T09:35:22Z 2018-07-20T09:35:22Z 2018-07-20 https://hdl.handle.net/10630/16315 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. eng open access Lenguajes de ordenador - Congresos A new self-organizing neural gas model based on Bregman divergences conference output