An Evolutionary and Graph-Based Method for Image Segmentation

  • Alessia Amelio
  • Clara Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)


A graph-based approach for image segmentation that employs genetic algorithms is proposed. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A fitness function, that extends the normalized cut criterion, is employed, and a new concept of nearest neighbor, that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is defined. Because of the locus-based representation of individuals, the method is able to partition images without the need to set the number of segments beforehand. As experimental results show, our approach is able to segment images in a number of regions that well adhere to the human visual perception.


Genetic Algorithm Image Segmentation Image Edge Meaningful Object Human Visual Perception 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Alessia Amelio
    • 1
    • 2
  • Clara Pizzuti
    • 1
  1. 1.Institute for High Performance Computing and NetworkingNational Research Council of Italy, CNR-ICARRendeItaly
  2. 2.DEISUniversità della CalabriaRendeItaly

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