Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets and Systems - Clustering and modeling
Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate distance oracles for graphs with dense clusters
Computational Geometry: Theory and Applications
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Partially supervised clustering for image segmentation
Pattern Recognition
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel multiseed nonhierarchical data clustering technique
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Pattern Recognition
Markovian approach using several Gibbs energy for remote sensing images segmentation
Analog Integrated Circuits and Signal Processing
A spatially constrained fuzzy hyper-prototype clustering algorithm
Pattern Recognition
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
Monte Carlo cluster refinement for noise robust image segmentation
Journal of Visual Communication and Image Representation
Segmentation for high-throughput image analysis: watershed masked clustering
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: applications and case studies - Volume Part II
Pattern Recognition Letters
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
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In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm's problem that the clustering quality is greatly affected by the data distributing and the stochastic initializing the centrals of clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial clustering centers adjacent to the actual final clustering centers can be found by the proposed single point adjustment method, which could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved.