Advertisement

Image Dominant Colors Estimation and Color Reduction Via a New Self-growing and Self-organized Neural Gas

  • A. Atsalakis
  • N. Papamarkos
  • I. Andreadis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

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.

Keywords

Color Space Neighboring Classis Neural Network Classifier Lateral Connection Color Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Heckbert, P.: Color image quantization for frame buffer display. Computer & Graphics 16, 297–307 (1982)CrossRefGoogle Scholar
  2. 2.
    Wan, S.J., Prusinkiewicz, P., Wong, S.K.M.: Variance based color image quantization for frame buffer display. Color Research and Application 15, 52–58 (1990)CrossRefGoogle Scholar
  3. 3.
    Ashdown, I.: Octree CQ, from the book: Radiosity-A Programmer’s Perspective. Wiley, New York (1994)Google Scholar
  4. 4.
    Wu, X.: CQ by dynamic programming and principal analysis. ACM Transactions on Graphics 11, 348–372 (1992)zbMATHCrossRefGoogle Scholar
  5. 5.
    Papamarkos, N., Atsalakis, A., Strouthopoulos, C.: Adaptive Color Reduction. IEEE Trans. On Systems, Man, and Cybernetics, Part B 32, 44–56 (2002)CrossRefGoogle Scholar
  6. 6.
    Baraldi, A., Blonda, P.: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition—Part I&II. IEEE Trans. On Systems, Man, and Cyb., Part B 29, 778–801 (1999)CrossRefGoogle Scholar
  7. 7.
    Carpenter, G., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)CrossRefGoogle Scholar
  8. 8.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  9. 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
  10. 10.
    Papamarkos, N., Atsalakis, A.: Gray-level reduction using local spatial features. Computer Vision and Image Understanding 78, 336–350 (2000)CrossRefGoogle Scholar
  11. 11.
    Papamarkos, N.: Color reduction using local features and a SOFM neural network. Int. Journal of Imaging Systems and Technology 10, 404–409 (1999)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T.: The self-organizing map. Proceedings of IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  13. 13.
    Dekker, A.H.: Kohonen neural networks for optimal CQ. Computation in Neural Systems 5, 351–367 (1994)zbMATHCrossRefGoogle Scholar
  14. 14.
    Huang, H.Y., Chen, Y.S., Hsu, W.H.: Color image segmentation using a self-organized map algorithm. Journal of Electronic Imaging 11, 136–148 (2002)CrossRefGoogle Scholar
  15. 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. 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
  17. 17.
    Martinetz, T.M., Schulten, K.J.: Topology representing networks. Neural Networks 7(3), 507–522 (1994)CrossRefGoogle Scholar
  18. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • A. Atsalakis
    • 1
  • N. Papamarkos
    • 1
  • I. Andreadis
    • 1
  1. 1.Image Processing and Multimedia Laboratory, Department of Electrical & Computer EngineeringDemocritus University of ThraceXanthiGreece

Personalised recommendations