An Improved Fast FCM Image Segmentation Algorithm Based on Region Feature Analysis
XU Shao-Ping1, LIU Xiao-Ping1,3, LI Chun-Quan1,2, HU Ling-Yan1, YANG Xiao-Hui1,2
1.School of Information Engineering,Nanchang University,Nanchang 330031 2.School of Mechanical and Electrical Engineering,Nanchang University,Nanchang 330031 3.Department of Systems and Computer Engineering,Carleton University,Ottawa,ON Canada K1S 5B6
Abstract A fast image segmentation algorithm based on region feature is proposed to estimate centroid number. In the preprocessing analysis stage, the feature vector based on the cooccurrence matrix statistics is used to describe the regional characteristics of sub-image, and the proposed algorithm combines with cluster validity function to estimate accurate centroid number and initialization of membership matrix. In the main clustering stage, the implicit feature of color and texture extracted by Gabor filter is used to accomplish clustering, which not only produces a more reasonable quality of region segmentation, but also has fine noise immunity. The experimental results show that the proposed algorithm effectively overcomes the deficiencies of pixel-level estimations, greatly accelerates the iterative speed of the FCM main clustering stage and achieves higher efficiency in the implementation.
XU Shao-Ping,LIU Xiao-Ping,LI Chun-Quan等. An Improved Fast FCM Image Segmentation Algorithm Based on Region Feature Analysis[J]. , 2012, 25(6): 987-995.
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2012, Vol. 25 
