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ABSTRACT: 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 same cluster. After fitting the B-spline-based mixed-effects model to the proposed multivariate t-distribution, we derive an expectation-maximization algorithm for estimating the parameters of the model, and apply the proposed approach to the simulated data and the real dataset. The experimental results show that our model yields better clustering results when compared to the conventional Gaussian random effect model.
IEEE Transactions on Neural Networks 01/2011; · 2.95 Impact Factor
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2010 IEEE International Conference on Granular Computing, GrC 2010, San Jose, California, USA, 14-16 August 2010; 01/2010
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Pattern Recognition. 01/2009; 42:2029-2044.
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ABSTRACT: In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and
accurate segmentation in case of mixed noise without using experimentally set parameters, called improved adaptive spatial
information clustering (IASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting
factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous
segmentation and reduces the edge-blurring effect. Furthermore, a unique characteristic of the new information segmentation
algorithm is that it has the capabilities to eliminate outliers at different stages of the IASIC algorithm. These result in
improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. The experimental
results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and
the algorithm outperforms other popular spatial clustering variants.
12/2008: pages 308-317;
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Advances in Visual Computing, 4th International Symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008. Proceedings, Part I; 01/2008
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10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008, Hanoi, Vietnam, 17-20 December 2008, Proceedings; 01/2008
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ABSTRACT: This paper presents a novel image segmentation algorithm that has a new dissimilarity measure which incorporates the spatial information. Our method uses a fully automatic technique to obtain the segmentation result and cluster number, and the new clustering objective function incorporates the spatial information and can compensate for the misclassification errors due to noise shifting. The capacity maximization and structure risk minimization are utilized to evaluate the quality of the clustering result via a trade-off between the number of unreliable data points and model complexity (i.e. cluster number). The weighting factor for neighborhood effect is adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous region and reduces the edge-blurring effect. The experimental results with synthetic and real images demonstrate that the proposed method is effective in determining the optimal cluster number and eliminating the noise artifact.
Neural Networks, 2006. IJCNN '06. International Joint Conference on; 01/2006
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ABSTRACT: The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.
Pattern Recognition.
