Texture Segmentation Using Intensified Fuzzy Kohonen Clustering Network

• Published in: Advances in Natural Computation pp. 75 - 80
• Main Authors: Liu, Dong, Tang, Yinggan, Guan, Xinping
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Fuzzy Factor; Kohonen Neural Network; Partition Performance; Pattern recognition. Digital image processing. Computational geometry; Segmentation Result; Texture Segmentation; Cluster analysis; Fuzzy logic; Segmentation; Image processing; Classification; Kohonen algorithm; Adaptive method; Fuzzy neural nets; Texture; Convergence speed
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_12

Fuzzy Kohonen clustering network(FKCN) shows great superiority in processing the clustering in image segmentation. In this paper, an intensified Fuzzy Kohonen clustering Network (IFKCN) is proposed for texture segmentation. The method adjusts fuzzy factors to accelerate the speed of convergence. It intensifies the biggest membership and suppresses the other. By using this network in Brodatz texture segmentation, its iteration is fewer and the speed of convergence is quicker than FKCN and AFKCN(Adaptive Fuzzy Kononen clustering Network), and segmentation results are as well as FKCN.