DocumentCode :
1741562
Title :
A new multiscale Bayesian model averaging framework for texture segmentation
Author :
Wan, Yi ; Nowak, Robert
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
509
Abstract :
In texture segmentation, in order to accurately classify any pixel a suitable neighborhood must be chosen. However, selecting a neighborhood size and orientation is a difficult and often ad hoc task. We view the task of choosing a neighborhood as model selection problem and develop a multiscale Bayesian model averaging (BMA) framework for pixel-level texture segmentation. This framework leads to a maximum a posteriori (MAP) segmentation rule that combines information from different neighborhoods (models) defined at multiple scales and locations. Thus, our new method avoids the unsatisfactory requirement of a user-specified notion of “neighborhood,” instead letting the data speak for themselves. The performance of the new segmentation algorithm is examined in simulated studies
Keywords :
Bayes methods; image classification; image segmentation; image texture; maximum likelihood estimation; BMA framework; MAP segmentation rule; images; maximum a posteriori segmentation rule; model selection problem; multiscale Bayesian model averaging framework; neighborhood orientation; neighborhood size; pixel-level texture segmentation; Bayesian methods; Filter bank; Hidden Markov models; Humans; Image segmentation; Laplace equations; Partitioning algorithms; Roads; Stochastic processes; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1522-4880
Print_ISBN :
0-7803-6297-7
Type :
conf
DOI :
10.1109/ICIP.2000.901007
Filename :
901007
Link To Document :
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