Paper Title:
Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering
  Abstract

Image segmentation is an important means of the implementation of image analysis. The existing segmentation methods have their own advantages and disadvantages in segmentation time and segmentation effect. Image segmentation based on fuzzy clustering and genetic algorithm is studied. An adaptive genetic algorithm is improved, the crossover rate and mutation rate are optimized, and a new adaptive operator is adopted to achieve a non-linear adaptive adjustment. A new combined image segmentation means is presented, in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. The practice proves that this image segmentation method and algorithm is superior to the traditional one, which improves the segmentation performance and the segmentation effect.

  Info
Periodical
Advanced Materials Research (Volumes 143-144)
Edited by
H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong
Pages
379-383
DOI
10.4028/www.scientific.net/AMR.143-144.379
Citation
J. Zhang, X. Zhang, J. Zhang, "Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering", Advanced Materials Research, Vols. 143-144, pp. 379-383, 2011
Online since
October 2010
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Price
$32.00
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