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Map Segmentation by Colour Cube Genetic K-Mean Clustering

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Research and Advanced Technology for Digital Libraries (ECDL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1923))

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Abstract

In this work, a method is described for evolving adaptive procedures for colour image segmentation. We formulate the segmentation problem as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms (GA) for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into GA, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of GA to automatic and unsupervised texture segmentation. Some examples in Colour Maps are presented and overall results discussed

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Author information

Authors and Affiliations

  1. CVRM - IST Geo-Systems Centre, Instituto Superior Técnico, Avenida Rovisco Pais, 1049-001, Lisboa, Portugal

    Vitorino Ramos & Fernando Muge

Authors
  1. Vitorino Ramos
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  2. Fernando Muge
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Editor information

Editors and Affiliations

  1. National Library of Portugal, Campo Grande, 83, 1749-081, Lisboa, Portugal

    José Borbinha

  2. GMD Library, Schloss Birlinghoven, 53754, Sankt Augustin, Germany

    Thomas Baker

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© 2000 Springer-Verlag Berlin Heidelberg

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Ramos, V., Muge, F. (2000). Map Segmentation by Colour Cube Genetic K-Mean Clustering. In: Borbinha, J., Baker, T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2000. Lecture Notes in Computer Science, vol 1923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45268-0_30

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  • DOI: https://doi.org/10.1007/3-540-45268-0_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41023-2

  • Online ISBN: 978-3-540-45268-3

  • eBook Packages: Springer Book Archive

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