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.

• Periodical: Advanced Materials Research (Volumes 143-144)
• Pages: 379-383
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.143-144.379
• Online since: October 2010
• Authors: Jing Zhang, Xiang Zhang, Jie Zhang
• Keywords: Combination, Fuzzy Clustering, Genetic, Image Segmentation, Improved Algorithm

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