Principles and practice of information theory
Principles and practice of information theory
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
On Discontinuity-Adaptive Smoothness Priors in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Information Clustering Algorithm
Neural Computation
Computer Vision and Image Understanding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A generalized Gaussian image model for edge-preserving MAP estimation
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
An information-theoretic fuzzy C-spherical shells clustering algorithm
Fuzzy Sets and Systems
Non-local spatial spectral clustering for image segmentation
Neurocomputing
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
A non-local fuzzy segmentation method: Application to brain MRI
Pattern Recognition
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Fuzzy spectral clustering with robust spatial information for image segmentation
Applied Soft Computing
An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation
Computer Vision and Image Understanding
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
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The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.