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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.
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