A new algorithm for unsupervised image segmentation based on D-MRF model and ANOVA

Author

Sun, Haiyan ; Wang, Wenwen

Author_Institution

Sch. of Math. & Syst. Sci., Beihang Univ., Beijing, China

fYear

2009

fDate

6-8 Nov. 2009

Firstpage

754

Lastpage

758

Abstract

A new algorithm for unsupervised image segmentation is proposed in this paper, which is based on the D-MRF model and ANOVA. Firstly, ANOVA is incorporated to determine the number of clusters combining with several statistics. Compared with models based on information criteria, ANOVA avoids the parameter estimation error, which reduces time consumption. Secondly, histogram is adopted to verify the validity of the new algorithm. Secondly, D-MRF is adopted to setup modeling. Thirdly, based on MRF-MAP, image segmentation is realized through using ICM. In model fitting, DAEM is used to estimate parameters in image field; on the other hand, local entropy is simulated as parameters in label field. Finally, the validity and practicability of the new algorithm are verified by two experiments.

Keywords

Markov processes; entropy; image segmentation; maximum likelihood estimation; parameter estimation; ANOVA; D-MRF model; MRF-MAP; Markov random fields; local entropy; parameter estimation error; setup modeling; unsupervised image segmentation; Analysis of variance; Clustering algorithms; Entropy; Histograms; Image analysis; Image segmentation; Markov random fields; Mathematical model; Parameter estimation; Pixel; ANOVA; D-MRF model; Image segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4898-2

Electronic_ISBN

978-1-4244-4900-6

Type

conf

DOI

10.1109/ICNIDC.2009.5360817

Filename

5360817