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In order to improve the robustness of the conventional fuzzy C-means (FCM) clustering algorithms for image segmentation, a robust information fuzzy clustering algorithm is proposed in this paper. This is an extension of the information-theoretic framework into the FCM-type algorithms. Combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the sensitivity...
This brief presents a curve clustering technique based on a new multivariate model. Instead of the usual Gaussian random effect model, our method uses the multivariate -distribution model which has better robustness to outliers and noise. In our method, we use the B-spline curve to model curve data and apply the mixed-effects model to capture the randomness and covariance of all curves within the...
In this paper, we present a novel image clustering algorithm that has a new dissimilarity measure which incorporates the adaptive spatial information. The spatial connectivity of an image is controlled by a weighting factor so that it enhances the smoothness towards piecewise-homogeneous region and reduces the edge-blurring effect. Our method also utilizes the capacity maximization to evaluate the...
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