Histogram clustering for unsupervised image segmentation

Author

Puzicha, Jan ; Hofmann, Thomas ; Buhmann, Joachim M.

Author_Institution

Inst. fur Inf. III, Bonn Univ., Germany

Volume

2

fYear

1999

fDate

1999

Abstract

This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms

Keywords

Bayes methods; image segmentation; optimisation; unsupervised learning; Bayesian framework; Gabor coefficients; K-means clustering; annealed maximum a posteriori estimation; benchmark results; distributional data; histogram clustering; histogram data; local distributions; multiscale formulation; optimal clustering; probabilistic grouping; proximity-based algorithms; statistical mixture model; unsupervised image segmentation; Acceleration; Annealing; Bayesian methods; Clustering algorithms; Clustering methods; Computer science; Histograms; Image segmentation; Maximum a posteriori estimation; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.

Conference_Location

Fort Collins, CO

ISSN

1063-6919

Print_ISBN

0-7695-0149-4

Type

conf

DOI

10.1109/CVPR.1999.784981

Filename

784981