A New Self-Organizing Neural Gas Model based on Bregman Divergences
Esteban J. Palomo, Miguel A. Molina‐Cabello, Ezequiel López‐Rubio, Rafael Marcos Luque‐Baena · 2018
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 self-organization capabilities.