Proceedings ArticleDOI
Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation
Daoqiang Zhang,Songcan Chen,Zhisong Pan,Keren Tan +3 more
- Vol. 4, pp 2189-2192
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TLDR
A kernel-based fuzzy clustering algorithm that exploits the spatial contextual information in image data that is more robust to noise than the conventional fuzzy image segmentation algorithms.Abstract:
The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, we present a kernel-based fuzzy clustering algorithm that exploits the spatial contextual information in image data. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm using a kernel-induced distance metric and a spatial penalty term that takes into account the influence of the neighboring pixels on the centre pixel. Experimental results on both synthetic and real MR images show that the proposed algorithm is more robust to noise than the conventional fuzzy image segmentation algorithms.read more
Citations
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Journal ArticleDOI
A survey of kernel and spectral methods for clustering
TL;DR: A survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters and an explicit proof of the fact that these two paradigms have the same objective is reported.
Journal ArticleDOI
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
TL;DR: An improved fuzzy C-means (FCM) algorithm for image segmentation is presented by introducing a tradeoff weighted fuzzy factor and a kernel metric and results show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
Journal Article
Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term
Yong Yang,Shuying Huang +1 more
TL;DR: To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper, which is inspired from the neighborhood expectation maximization algorithm.
Book ChapterDOI
Semi-supervised Learning for Cyberbullying Detection in Social Networks
TL;DR: The experimental results indicate that the proposed augmented approach outperformed all other methods, and is suitable in the real-world situations, where sufficiently labelled instances are not available for training.
Journal ArticleDOI
Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation
TL;DR: A novel similarity measure model is established based on image patches and local statistics, and the neighbourhood-weighted distance is defined to replace the Euclidean distance in the objective function of FCM.
References
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Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
An introduction to kernel-based learning algorithms
TL;DR: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.
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A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic and the neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings.
Journal ArticleDOI
Adaptive segmentation of MRI data
TL;DR: Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging data, that has proven to be effective in a study that includes more than 1000 brain scans.
Book ChapterDOI
Adaptive Segmentation of MRI Data
TL;DR: Adaptive segmentation is described, a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.
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