Neighborhood weight fuzzy c-means kernel clustering based infrared image segmentation

Aiming for the feature of low resolution and faint contrast for infrared image, a segmentation algorithm is presented based on the neighborhood weight fuzzy c-means kernel clustering. By using the Gaussian kernel in target function, the traditional euclidean distance in the FCM is replaced by a kernel-induced distance. At the same time, this method computes the sample weight during the clustering procedure by considering the pixel's neighborhood. On this basis, a new iteration formula is deduced. The experimental results show that the method given by this paper, is better than the standard algorithm, and can segment the infrared image which is polluted by noise effectively.