Color Reduction by Using a new Self-Growing and Self-Organized Neural Network

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Date
2005
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the developed of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and Self- Organized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. As a consecutive, isolated color classes, which may correspond to significant image details, can be obtained. The SGONG is fed by the color components and additional spatial features. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space.
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@inproceedings{
10.2312:vvg.20051007
, booktitle = {
Vision, Video, and Graphics (2005)
}, editor = {
Mike Chantler
}, title = {{
Color Reduction by Using a new Self-Growing and Self-Organized Neural Network
}}, author = {
Atsalakis, A.
and
Papamarkos, N.
}, year = {
2005
}, publisher = {
The Eurographics Association
}, ISBN = {
3-905673-57-6
}, DOI = {
10.2312/vvg.20051007
} }
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