Image segmentation based on kernel fuzzy C means clustering using edge detection method on noisy images
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 2
Abstract
Classical fuzzy C-means (FCM) clustering is performed in the input space, given the desired number of clusters. Although it has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, a novel kernel-based fuzzy C-means clustering algorithm (KFCM). Its basic idea is to transform implicitly the input data into a higher dimensional feature space via a nonlinear map, which increases greatly possibility of linear separability of the patterns in the feature space, then perform FCM in the feature space. Another good attribute of KFCM is that it can automatically estimate the number of clusters in the dataset. A survey on clustering algorithms and emphasis on kernel based FCM is provided , since through a nonlinear map it wisely increases the linear separability of data points. Hence KFCM provide a suitable solution for segmenting images into subimages. The numerically complex level set method for extracting boundaries has paved a way for proposing Canny Edge Detection Algorithm for accurate results and reduction of computational complexity.
Authors and Affiliations
Saritha A K , Ameera P. M
Keywords
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