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A novel approach for segmentation of images has been proposed by incorporating the advantages of the mean shift segmentation and the normalized cut partitioning methods. The proposed method preprocesses an image by using the mean shift algorithm to form segmented regions, region nodes are applied to form the weight matrix W instead of these regions, the Ncut method is then introduced for region nodes...
In this paper, we present new perceptual techniques for segmentation and annotation of natural images. The image segmentation approach is a multilevel clustering method based on a new proposed non-parametric clustering algorithm, called adaptive medoidshift (AMS) and normalized cuts (N-cut). The AMS method locally clusters the image color composition by considering their spatial distribution into...
A method of region-based image segmentation with mean-shift clustering algorithm is introduced. This method first extracts color, texture, and location features from each pixel to form feature vector by selecting suitable color space. Then, these feature vectors are clustering with mean-shift clustering algorithm and the window parameter r is decided by the proposed method of selecting optimal clustering...
In this paper, the radial basis vector (RBV) is proposed to describe the descriptor set of an image. And the shared nearest neighbor clustering kernel (SNNCK) technique is proposed to match RBV pairs. SNNCK is based on the charge attractive model, which will make the unequal-dimensional data sets clustering naturally. Thus, this novel algorithm is able to match the unequal-dimensional data sets when...
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