Visual reconstruction
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts
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
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Journal of Machine Learning Research
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-dependent segmentation and matching in image databases
Computer Vision and Image Understanding
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian Feature and Model Selection for Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Based Modeling by Joint Segmentation
International Journal of Computer Vision
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising using mixtures of projected Gaussian scale mixtures
IEEE Transactions on Image Processing
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Stochastic approximation algorithms for partition function estimation of Gibbs random fields
IEEE Transactions on Information Theory
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
MAP image restoration and segmentation by constrained optimization
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Segmentation of brain images using adaptive atlases with application to ventriculomegaly
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
A finite mixture model for detail-preserving image segmentation
Signal Processing
Unsupervised classification of SAR images using normalized gamma process mixtures
Digital Signal Processing
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
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A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise.