An Evolutionary Approach for Image Segmentation (Articolo in rivista)

Type
Label
  • An Evolutionary Approach for Image Segmentation (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1162/EVCO_a_00115 (literal)
Alternative label
  • Amelio, Alessia; Pizzuti, Clara (2014)
    An Evolutionary Approach for Image Segmentation
    in Evolutionary computation
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Amelio, Alessia; Pizzuti, Clara (literal)
Pagina inizio
  • 525 (literal)
Pagina fine
  • 557 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 33 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 4 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • National Research Council Italy, CNR (literal)
Titolo
  • An Evolutionary Approach for Image Segmentation (literal)
Abstract
  • The paper explores the use of evolutionary techniques in dealing with the image segmentation problem. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A genetic algorithm that uses a fitness function based on an extension of the normalized cut criterion is proposed. The algorithm employs the locus-based representation of individuals, which allows for the partitioning of images without setting the number of segments beforehand. 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 also defined. Experimental results show that our approach is able to segment images in a number of regions that conform well to human visual perception. The visual perceptiveness is substantiated by objective evaluation methods based on uniformity of pixels inside a region, and comparison with ground-truth segmentations available for part of the used test images. (literal)
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