A deterministic annealing approach to clustering
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields
Pattern Recognition Letters
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
A modified mountain clustering algorithm
Pattern Analysis & Applications
Clustering by competitive agglomeration
Pattern Recognition
An investigation of mountain method clustering for large data sets
Pattern Recognition
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Robust fusion of uncertain information
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Recovery Rate of Clustering Algorithms
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A robust iterative refinement clustering algorithm with smoothing search space
Knowledge-Based Systems
A variant of adaptive mean shift-based clustering
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A class boundary preserving algorithm for data condensation
Pattern Recognition
Sample-weighted clustering methods
Computers & Mathematics with Applications
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
Clustering via geometric median shift over Riemannian manifolds
Information Sciences: an International Journal
Dynamics of a mean-shift-like algorithm and its applications on clustering
Information Processing Letters
Clustering construction on a multimodal probability model
Information Sciences: an International Journal
Concurrent photo sequence organization
Multimedia Tools and Applications
Robotics and Autonomous Systems
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In this paper, a mean shift-based clustering algorithm is proposed. The mean shift is a kernel-type weighted mean procedure. Herein, we first discuss three classes of Gaussian, Cauchy and generalized Epanechnikov kernels with their shadows. The robust properties of the mean shift based on these three kernels are then investigated. According to the mountain function concepts, we propose a graphical method of correlation comparisons as an estimation of defined stabilization parameters. The proposed method can solve these bandwidth selection problems from a different point of view. Some numerical examples and comparisons demonstrate the superiority of the proposed method including those of computational complexity, cluster validity and improvements of mean shift in large continuous, discrete data sets. We finally apply the mean shift-based clustering algorithm to image segmentation.