Journal ArticleDOI
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
TLDR
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.Abstract:
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. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.read more
Citations
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Journal ArticleDOI
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images
TL;DR: An unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates that demonstrates the promising performance of the proposed network compared with several existing approaches.
Journal ArticleDOI
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering
TL;DR: An improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper and demonstrates that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Journal ArticleDOI
Feature learning and change feature classification based on deep learning for ternary change detection in SAR images
TL;DR: In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison, and results on real datasets validate the effectiveness and superiority of the proposed framework.
Journal ArticleDOI
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation
TL;DR: A superpixel-based fast FCM clustering algorithm that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation and implemented with histogram parameter on the superpixel image is proposed.
Journal ArticleDOI
DSets-DBSCAN: A Parameter-Free Clustering Algorithm.
Jian Hou,Huijun Gao,Xuelong Li +2 more
TL;DR: This work investigates the characteristics of existing clustering algorithms and presents a parameter-free algorithm based on the DSets (dominant sets) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms that performs better than or comparably with other algorithms with careful parameter tuning.
References
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Book
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
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
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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