Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences - Palomo-Ferrer, Esteban José; Benito-Picazo, Jesús; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; Ortega-Zamorano, Francisco (Springer, 2021)
In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman 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 quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.
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Keywords: Redes neuronales artificiales; Color quantization; Clustering; Neural networks; Self-organization