Image segmentation algorithm of fuzzy clustering based on ant colony and adaptive filtering
As fuzzy C-means clustering (FCM) algorithm is sensitive to the initial clustering centre,and lacks enough robustness and also has big computational cost,an novel image segmentation algorithm based on ant colony and histogram fuzzy clustering is proposed.Firstly,the algorithm determines the initial clustering centre as the original parameter of FCM using ant colony algorithm,so as to overcome the sensitivity to the initial clustering centre.Secondly,the algorithm restrains the interference of image noise and enhances the robustness of algorithm by adaptive median filter.Finally,the algorithm optimizes the objective function of FCM with characteristic space of histogram in order to reduce calculation.Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM,which brings high robustness and segmentation accuracy,and has more faster convergence speed.
FCM clustring algorithmant algorithmimage segmentationadaptive median filtercharacter of histogram
NETL
NSTL
万方数据
维普
