Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6952052 | Digital Signal Processing | 2015 | 9 Pages |
Abstract
Conventional Fuzzy C-means (FCM) algorithm uses Euclidean distance to describe the dissimilarity between data and cluster prototypes. Since the Euclidean distance based dissimilarity measure only characterizes the mean information of a cluster, it is sensitive to noise and cluster divergence. In this paper, we propose a novel fuzzy clustering algorithm for image segmentation, in which the Mahalanobis distance is utilized to define the dissimilarity measure. We add a new regularization term to the objective function of the proposed algorithm, reflecting the covariance of the cluster. We experimentally demonstrate the effectiveness of the proposed algorithm on a generated 2D dataset and a subset of Berkeley benchmark images.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Xuemei Zhao, Yu Li, Quanhua Zhao,