Though FCM has long been widely used in image segmentation, it yet faces several challenges. Traditional FCM needs a laborious process to decide cluster center number by repetitive tests. Moreover, random initialization of cluster centers can let the algorithm easily fall onto local minimum, causing the segmentation results to be suboptimal. Traditional FCM is also sensitive to noise due to the reason that the pixel partitioning process goes completely in the feature space, ignoring some necessary spatial information. In this paper we introduce a modified FCM algorithm for color image segmentation. The proposed algorithm adopts an adaptive and robust initialization method which automatically decides initial cluster center values and center number according to the input image. In addition, by deciding the window size of pixel neighbor and the weights of neighbor memberships according to local color variance, the proposed approach adaptively incorporates spatial information to the clustering process and increases the algorithm robustness to noise pixels and drastic color variance. Experimental results have shown the superiority of modified FCM over traditional FCM algorithm.
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