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In image clustering, digital images can be represented with a large number of visual features corresponding to a high dimensional data space. Traditional clustering algorithms have difficulty in processing image dataset because of the curse of dimensionality. Moreover, similarity between images is measured by the values of partial features. To discover clusters existing in different subspace is known...
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...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
In kernel-based algorithms, Mercer kernel techniques have been used for improving the separability of input patterns. Although designed to tackle the problem of curse of dimensionality, non-accelerated kernel-based clustering algorithms fail to provide enough time efficiency for practical applications, such as medical image segmentation. For improving the time efficiency of kernel-based clustering,...
We propose a randomized algorithm of spectral clustering and apply it to appearance-based image/video segmentation. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. However, its high computational expensive restricts the applications. Our algorithm exploits random projection and subsampling techniques for reducing dimensionality and cardinality of data...
In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image...
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