Image segmentation is one of the most difficult and challenging problems in image processing which is widely used in a variety of applications such as robot vision, object recognition, geographical imaging, and medical imaging. 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 problem, a robust fuzzy clustering-based image segmentation method for noisy image(RFCM) is proposed. 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 the non-Euclidean structure of the input data due to the use of Euclidean distance(L2 norm). In this project, a robust image segmentation algorithm using fuzzy clustering based on kernel-induced distance measure which extends the RFCM algorithm to corresponding kernelled version KRFCM by the kernel methods is developed using Matlab. 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. Reference Paper: Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure
Author’s Name: Yanling LI and Yi SHEN
Source: IEEE
Year:2008
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