Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is noise sensitive because of not taking into account the spatial information in the image. To overcome the above problem, Z. Yang, et al. propose a robust fuzzy clustering based image segmentation method for noisy image(RFCM). Although the RFCM algorithm is insensitivity to noise to some extent, it still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, we propose a robust image segmentation algorithm using fuzzy clustering based on kernel-induced distance measure which extends RFCM algorithm to corresponding kernelled version KRFCM by the kernel methods. The KRFCM algorithm includes a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data. The experiments show that KRFCM can segment images more effectively and provide more robust segmentation results.