Abstract
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.
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© 1998 Springer-Verlag Wien
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Gonzalez, A.I., Graña, M., Lozano, J.A., Larrañaga, P. (1998). Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_123
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_123
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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