RT Conference Proceedings
T1 A new self-organizing neural gas model based on Bregman divergences
A1 Palomo-Ferrer, Esteban José
A1 Molina-Cabello, Miguel Ángel
A1 López-Rubio, Ezequiel
A1 Luque-Baena, Rafael Marcos
K1 Lenguajes de ordenador - Congresos
AB In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman NeuralGas (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 selforganizationcapabilities.
YR 2018
FD 2018-07-20
LK https://hdl.handle.net/10630/16315
UL https://hdl.handle.net/10630/16315
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 feb 2026