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A Genetic Algorithm for Color Image Segmentation

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Applications of Evolutionary Computation (EvoApplications 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7835))

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Abstract

A genetic algorithm for color image segmentation is proposed. The method represents an image as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels is computed by taking into account not only brightness, but also color and texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that the method is able to partition natural and human scenes in a number of regions consistent with human visual perception. A quantitative evaluation of the method compared with other approaches shows that the genetic algorithm can be very competitive in partitioning color images.

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Author information

Authors and Affiliations

  1. Institute for High Performance Computing and Networking, National Research Council of Italy, CNR-ICAR, Via P. Bucci 41C, 87036, Rende, CS, Italy

    Alessia Amelio & Clara Pizzuti

Authors
  1. Alessia Amelio
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  2. Clara Pizzuti
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Editor information

Editors and Affiliations

  1. S2 Grupo, C/ Ramiro de Maeztu 7, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

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Amelio, A., Pizzuti, C. (2013). A Genetic Algorithm for Color Image Segmentation. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_32

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  • DOI: https://doi.org/10.1007/978-3-642-37192-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37191-2

  • Online ISBN: 978-3-642-37192-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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