Feature symbol random field for texture segmentation

Author

Ma, Xiaochuan ; Zhao, Rongchun ; Hou, Chaohuan

Author_Institution

Inst. of Acoust., Acad. Sinica, Beijing, China

Volume

2

fYear

1997

fDate

28-31 Oct 1997

Firstpage

999

Abstract

This paper defines the feature symbol random field (FSRF) of feature texture images and suggests a novel FSRF-Gibbs model for texture segmentation. The advantages of FSRF are that it can generalize the spatial changed texture feature vectors which come from multichannel analysis, meanwhile, significantly easing the estimation problem of MRF. As a result, finer and more reasonable segmentation is expected by involving both multichannel analysis techniques and a fine Markov random field (MRF) model. A new algorithm is also proposed, which is easy to calculate and yields satisfactory experimental results on Brodatz textures

Keywords

Markov processes; feature extraction; image segmentation; image texture; Brodatz textures; Gibbs model; Markov random field model; estimation problem; feature symbol random field; feature texture images; multichannel analysis; spatial changed texture feature vectors; texture segmentation; Acoustic testing; Books; Chaos; Clustering algorithms; Computer science; Filtering; Filters; Image segmentation; Image texture analysis; Markov random fields; Probability; Production; Radio frequency;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.669122

Filename

669122