An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
An introduction to variational methods for graphical models
Learning in graphical models
International Journal of Computer Vision
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
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Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Measure for Objective Evaluation of Image Segmentation Algorithms
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Nonparametric Bayesian Image Segmentation
International Journal of Computer Vision
Signal Modeling and Classification Using a Robust Latent Space Model Based on Distributions
IEEE Transactions on Signal Processing
Music Analysis Using Hidden Markov Mixture Models
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
The infinite Student's t-mixture for robust modeling
Signal Processing
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
A spatially-constrained normalized Gamma process prior
Expert Systems with Applications: An International Journal
Unsupervised color images segmentation using spatial hidden MRF GDPM model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Margin-maximizing classification of sequential data with infinitely-long temporal dependencies
Expert Systems with Applications: An International Journal
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Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation procedure. Existing methods, including likelihood- or entropy-based criteria, and reversible Markov chain Monte Carlo methods, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (DP, infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori; infinite mixture models based on the original DP or spatially constrained variants of it have been applied in unsupervised image segmentation applications showing promising results. Under this motivation, to resolve the aforementioned issues of HMRF models, in this paper, we introduce a nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model, formulated on the basis of a joint Dirichlet process mixture (DPM) and Markov random field (MRF) construction. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally demonstrate its advantages over competing methodologies.