Bayesian image segmentation with mean shift

Author

Zhou, Huiyu ; Schaefer, Gerald ; Celebi, M. Emre ; Fei, Minrui

Author_Institution

Queen´´s Univ. Belfast, Belfast, UK

fYear

2009

fDate

7-10 Nov. 2009

Firstpage

2405

Lastpage

2408

Abstract

Image segmentation plays a key role in many image content analysis applications, and a lot of effort has aimed at improving the performance of established segmentation algorithms. In this paper, we present a mean shift-based combined Dirichlet process mixture (MDP)/Markov Random Field (MRF) image segmentation algorithm. Our method incorporates a mean shift process to iteratively reduce the difference between the mean of cluster centres and image pixels within the standard MDP/MRF procedure. Experimental results show that the proposed segmentation technique outperforms the classical MDP/MRF algorithm.

Keywords

Bayes methods; Markov processes; image segmentation; pattern clustering; Bayesian image segmentation; Dirichlet process mixture; Markov random field; cluster centres; image content analysis; image pixels; mean shift; Bayesian methods; Clustering algorithms; Image analysis; Image segmentation; Iterative algorithms; Markov random fields; Merging; Monte Carlo methods; State estimation; Uncertainty; Dirichlet process mixture; Image segmentation; Markov Random Field; mean shift;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2009 16th IEEE International Conference on

Conference_Location

Cairo

ISSN

1522-4880

Print_ISBN

978-1-4244-5653-6

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2009.5414171

Filename

5414171