Image Dominant Colors Estimation and Color Reduction Via a New Self-growing and Self-organized Neural Gas
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.
KeywordsColor Space Neighboring Classis Neural Network Classifier Lateral Connection Color Quantization
- 3.Ashdown, I.: Octree CQ, from the book: Radiosity-A Programmer’s Perspective. Wiley, New York (1994)Google Scholar
- 9.Buhmann, J.M., Fellner, D.W., Held, M., Kettere, J., Puzicha, J.: Dithered CQ. In: Proceedings of the EUROGR 1998, Lisboa, Computer Graphics Forum, vol. 17(3), pp. 219–231 (1998)Google Scholar
- 15.Rahman, A., Rahman, C.M.: A new approach for compressing color images using neural network. In: Processing of CIMCA, Vienna, Austria, pp. 12–14 (2003)Google Scholar
- 16.Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
- 18.Comaniciu, D., Meer, P.: Mean Shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5) (2002)Google Scholar