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Construction of a reliable similarity matrix is fundamental for graph-based clustering methods. However, most of the current work is built upon some simple manifold structure, whereas limited work has been conducted on nonlinear data sets where data reside in a union of manifolds rather than a union of subspaces. Therefore, we construct a similarity graph to capture both global and local manifold...
Estimation of bias field together with the tissue class of a noisy Magnetic Resonance image has been a challenging task because of the nonlinear nature of bias field. In order to address this issue we have proposed two new schemes. The first one is the recursive framework, where class labels and bias fields have been estimated simultaneously. In one part of the recursion, a variable variance Adaptive...
The fuzzy c-means (FCM) algorithm is a very popular algorithm in the field of image segmentation because of its simplicity and less sensitivity to noise and it is widely used in the field of engineering disciplines. The FCM membership function can handle the overlapped clusters efficiently with predefined number of clusters, but this algorithm are unable to cluster non-linearly separable data as well...
The paper proposes a novel non-homogeneity measure based kernelized image segmentation algorithm for noisy images. Every 3×3 neighbourhood of every single pixel is considered for generating localized spatial domain non-homogeneity measures for every individual window. Then these spatial domain non-homogeneity measures are converted into fuzzy domain non-homogeneity coefficients by aggregating the...
In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels...
We consider the problem of learning the combination of multiple kernels given noisy pairwise constraints, which is in contrast to most of the existing studies that assume perfect pairwise constraints. This problem is particularly important when the pairwise constraints are derived from side information such as hyperlinks and paper citations. We propose a probabilistic approach for learning the combination...
Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but...
Fuzzy c-means (FCM) algorithm is considered as suitable algorithm for data clustering. However, the FCM has considerable trouble in a noisy environment and are inaccurate with large numbers of different sample sized clusters, because of its Euclidean distance measure objective function for finding the relationship between the objects. Those drawbacks can be solved by the Gaussian kernel mapping of...
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid...
This paper presents a new approach to find the optimal number of clusters of a fuzzy partition. It is based on a fuzzy modeling approach which combines measures of clusters' separation and overlap. Theses measures are based on triangular norms and a discrete Sugeno integral. Results on artificial and real data sets prove its efficiency compared to indexes from the literature.
In allusion to the disadvantages that fuzzy c-means algorithm is sensitive to noise and possibilistic c-means is easy to generate superposition cluster center, a novel algorithm (FPCM) which simultaneously produces both memberships and possibilities was proposed in 1997. However, FPCM still uses a norm-induced distance, as a consequence, its performance on the noisy data is not strong enough. In this...
A denoising algorithm for point-sampled geometry is proposed based on noise intensity. The noise intensity of each point on point-sampled geometry (PSG) is first measured by using a combined criterion. Based on mean shift clustering, the PSG is then clustered in terms of the local geometry-features similarity. According to the cluster to which a sample point belongs, a moving least squares surface...
Density based clustering technique like DBSCAN finds arbitrary shaped clusters along with noisy outliers. DBSCAN finds the density at a point by counting the number of points falling in a sphere of radius epsi and it has a time complexity of O(n2). Hence it is not suitable for large data sets. The proposed method in this paper is an efficient and fast Parzen-Window density based clustering method...
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