An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
An introduction to variational methods for graphical models
Learning in graphical models
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Nonparametric Bayesian Image Segmentation
International Journal of Computer Vision
The infinite hidden Markov random field model
IEEE Transactions on Neural Networks
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
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
The mean field theory in EM procedures for blind Markov random field image restoration
IEEE Transactions on Image Processing
Hi-index | 12.05 |
In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior.