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A Comparison of Experimental Results with an Evolution Strategy and Competitive Neural Networks for Near Real-Time Color Quantization of Image Sequences

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Abstract

Color quantization of image sequences is a case of non-stationary clustering problem. The approach we adopt to deal with this kind of problems is to propose adaptive algorithms to compute the cluster representatives. We have studied the application of Competitive Neural Networks and Evolution Strategies to the one-pass adaptive solution of this problem. One-pass adaptation is imposed by the near real-time constraint that we try to achieve. In this paper we propose a simple and effective evolution strategy for this task. Two kinds of competitive neural networks are also applied. Experimental results show that the proposed evolution strategy can produce results comparable to that of competitive neural networks.

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Authors and Affiliations

  1. Dept. CCIA, Univ. Pais Vasco/EHU, Aptdo 649, 20080, San Sebastián, Espãna

    A.I. Gonzalez, M. Grãna, A. D'Anjou, F.X. Albizuri & F.J. Torrealdea

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  1. A.I. Gonzalez
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  2. M. Grãna
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  3. A. D'Anjou
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  4. F.X. Albizuri
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  5. F.J. Torrealdea
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Gonzalez, A., Grãna, M., D'Anjou, A. et al. A Comparison of Experimental Results with an Evolution Strategy and Competitive Neural Networks for Near Real-Time Color Quantization of Image Sequences. Applied Intelligence 8, 43–51 (1998). https://doi.org/10.1023/A:1008268514617

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  • DOI: https://doi.org/10.1023/A:1008268514617

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