Image dominant colors estimation and color reduction via a new self-growing and self-organized neural gas

Authors:
A. Atsalakis;N. Papamarkos;I. Andreadis
Affiliations:
Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece;Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece;Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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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 development 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. Besides, a new method is proposed for the Estimation of the Most Important of already created Classes (EMIC). The combination of SGONG and EMIC in color images results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. 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.