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Dynamic Topology Networks for Colour Image Compression

  • Ezequiel López-Rubio
  • José Muñoz-Pérez
  • José Antonio Gómez-Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

The Self-Organizing Dynamic Graph (SODG) is a novel unsupervised neural network that overcomes some of the limitations of the Kohonen’s Self-Organizing Feature Map (SOFM) by using a dynamic topology among neurons. In this paper an application of the SODG to colour image compression is studied. A Huffman coding and the Lempel-Ziv algorithm are applied to the output of the SODG to provide considerable improvements in compression rates with respect to standard competitive learning. Furthermore, this system is shown to give mean squared errors of the reconstructed images similar to those of competitive learning. Experimental results are presented to illustrate the performance of this system.

Keywords

Weight Vector Image Compression Compression Rate Competitive Learning Huffman Code 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ezequiel López-Rubio
    • 1
  • José Muñoz-Pérez
    • 1
  • José Antonio Gómez-Ruiz
    • 1
  1. 1.Computer Science DepartmentUniversity of MálagaMálagaSpain

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