A cluster grouping technique for texture segmentation

Author

Manduchi, Roborto

Author_Institution

Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA

Volume

3

fYear

2000

fDate

2000

Firstpage

1060

Abstract

We propose an algorithm for texture segmentation based on a divide-and-conquer strategy of statistical modeling. Selected sets of Gaussian clusters, estimated via expectation maximization on the texture features, are grouped together to form composite texture classes. Our cluster grouping technique exploits the inherent local spatial correlation among posterior distributions of clusters belonging to the same texture class. Despite its simplicity, this algorithm can model even very complex distributions, typical of natural outdoor images

Keywords

Gaussian processes; correlation methods; divide and conquer methods; image segmentation; image texture; optimisation; statistical analysis; Gaussian clusters; cluster grouping; divide-and-conquer strategy; expectation maximization algorithm; image segmentation; image textures; spatial correlation; statistical modeling; texture segmentation; Bayesian methods; Clustering algorithms; Context modeling; Cost function; Image segmentation; Layout; Maximum likelihood estimation; Parameter estimation; Spatial coherence; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.903728

Filename

903728