Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87869-6_31 | 16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021) |
Keywords | DocType | Volume |
Color quantization, Clustering, Neural networks, Self-organization | Conference | 1401 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esteban J. Palomo | 1 | 95 | 14.79 |
| Jesús Benito-Picazo | 2 | 1 | 3.05 |
| Enrique Domínguez | 3 | 133 | 21.24 |
| Ezequiel López-Rubio | 4 | 323 | 39.73 |
| Francisco Ortega-Zamorano | 5 | 64 | 8.19 |