Regularized fuzzy clustering for fast image segmentation

Fuzzy clustering is a popular method for image segmentation and various of models based on fuzzy clustering are proposed. However, many methods suffer from the slow convergence and sensitivity to noise and parameters. In this letter, a novel fuzzy clustering method for image segmentation is proposed to solve these problems. A kernel which incorporates the local spatial information is proposed to regularize the membership partition matrix, the convolution operation between the proposed kernel and membership partition matrix greatly decreases the computational complexity. Because of the proposed kernel, the local neighbor information can be flexibly used, which makes the proposed algorithm robust to noise. Furthermore, the proposed algorithm does not depend on the preprocessing and empirically adjusted parameters any more. Experimental results show that the proposed algorithm is robust to noise, very fast and efficient.