Effective Image Segmentation using a Locally Weighted Fuzzy C-Means Clustering
2012, 17(12), pp.83-93
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient (Vpc), partition entropy (Vpe), Xie-Bdni function (Vxb) and Fukuyama-Sugeno function (Vfs). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information,fast generation FCM) in the cluster validity functions as well as both compactness and separation.
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