RT Conference Proceedings
T1 Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences
A1 Palomo-Ferrer, Esteban José
A1 Benito-Picazo, Jesús
A1 Domínguez-Merino, Enrique
A1 López-Rubio, Ezequiel
A1 Ortega-Zamorano, Francisco
K1 Redes neuronales artificiales
AB In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing HierarchicalBregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantizationof the original image. Experimental results confirm the color quantization capabilities of this approach.
PB Springer
YR 2021
FD 2021-09
LK https://hdl.handle.net/10630/22954
UL https://hdl.handle.net/10630/22954
LA eng
NO https://www.springernature.com/gp/open-science/policies/book-policies
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026