Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering

  • A. I. Gonzalez
  • M. Graña
  • J. A. Lozano
  • P. Larrañaga
Conference paper


Non-stationary clustering deals with the clustering of a sequence of data samples obtained at a diverse time instant. A paradigm case of non-stationary clustering is the color quantization of image sequences. We propose an efficient evolution strategy to compute adaptively the color representatives for each image in the sequence.


Evolution Strategy Mutation Operator Vector Quantization Cluster Problem Image 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.


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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • A. I. Gonzalez
    • 1
  • M. Graña
    • 1
  • J. A. Lozano
    • 1
  • P. Larrañaga
    • 1
  1. 1.Dpt. CCIA Univ. Pais Vasco/EHUSan SebastiánEspaña

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