Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Application of Wavelet Threshold to Image De-noising
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Nonparametric Bayesian Image Segmentation
International Journal of Computer Vision
Image denoising with an optimal threshold and neighbouring window
Pattern Recognition Letters
IRGS: Image Segmentation Using Edge Penalties and Region Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
A finite mixtures algorithm for finding proportions in SAR images
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper explores a stochastic approach to refining clustering results for data with spatial-feature context such as images under the presence of noise. We formulate the clustering problem as a maximum a posteriori (MAP) problem, and refine clustering results using importance-weighted Monte Carlo posterior estimates based on between-neighborhood error statistics to account for local spatial-feature context within a global framework. This cluster refinement approach is non-iterative and can be integrated with existing clustering methods to achieve improved clustering performance for image segmentation under high noise scenarios. Experiments on synthetic gray-level images, real-world natural images, and real-world satellite synthetic aperture radar imagery illustrate the proposed method's potential for improving clustering performance of existing clustering algorithms for image segmentation under high noise situations.