A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering

Authors:
Sotirios P. Chatzis
Affiliations:
Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, 33 Saripolou Street, P.O. Box 50329, CY3603 Limassol, Cyprus
Venue:
Pattern Recognition
Year:
2013

Citing 17
Cited 0

An iterative Gibbsian technique for reconstruction of m-ary images

Pattern Recognition
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Measures of Similarity

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
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
A hierarchical Bayesian language model based on Pitman-Yor processes

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Nonparametric Bayesian Image Segmentation

International Journal of Computer Vision
The infinite hidden Markov random field model

IEEE Transactions on Neural Networks
Supervised hierarchical Pitman-Yor process for natural scene segmentation

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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
A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

IEEE Transactions on Fuzzy Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
The mean field theory in EM procedures for blind Markov random field image restoration

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this work, we propose a Markov random field-regulated Pitman-Yor process (MRF-PYP) prior for nonparametric clustering of data with spatial interdependencies. The MRF-PYP is constructed by imposing a Pitman-Yor process over the distribution of the latent variables that allocate data points to clusters (model states), the discount hyperparameter of which is regulated by an additionally postulated simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. Further, based on the stick-breaking construction of the Pitman-Yor process, we derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an unsupervised image segmentation application using a real-world dataset. We show that our approach completely outperforms related methods from the field of Bayesian nonparametrics, including the recently proposed infinite hidden Markov random field model and the Dirichlet process prior.