Smooth image segmentation by nonparametric bayesian inference

Authors:
Peter Orbanz;Joachim M. Buhmann
Affiliations:
Institute of Computational Science, ETH Zurich;Institute of Computational Science, ETH Zurich
Venue:
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Year:
2006

Citing 6
Cited 4

Boundary Detection by Constrained Optimization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram clustering for unsupervised segmentation and image retrieval

Pattern Recognition Letters
Bayesian extension to the language model for ad hoc information retrieval

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Stability-based validation of clustering solutions

Neural Computation
Variational methods for the Dirichlet process

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bayesian inference for multiband image segmentation via model-based cluster trees

Image and Vision Computing

Category Level Object Segmentation by Combining Bag-of-Words Models with Dirichlet Processes and Random Fields

International Journal of Computer Vision
Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet Process Mixture Models

International Journal of Computer Vision
Online video segmentation by bayesian split-merge clustering

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Unsupervised classification of SAR images using normalized gamma process mixtures

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assignments. The nonparametric nature of this model is implemented by a Dirichlet process prior to control the number of clusters. The resulting posterior can be sampled by a modification of a conjugate-case sampling algorithm for Dirichlet process mixture models. This sampling procedure estimates segmentations as efficiently as clustering procedures in the strictly conjugate case. The sampling algorithm can process both single-channel and multi-channel image data. Experimental results are presented for real-world synthetic aperture radar and magnetic resonance imaging data.