Fuzzy Clustering Color Image Segmentation Algorithm Based on CPSO

Abstract:

Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.

• Periodical: Advanced Materials Research (Volume 461)
• Pages: 526-531
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.461.526
• Online since: February 2012
• Authors: Xiao Hong Zhang, Hong Mei Ning
• Keywords: Chaos Particle Swarm Optimization, Color Image Segmentation, Feature Distance, Fuzzy C-Mean Clustering

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