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Proceedings Paper

Bayesian classification validation scheme driven by a localized/low-resolution Bhattacharyya distance classifier
Author(s): Emerson Prado Lopes
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Paper Abstract

A scheme for comparative performance analysis of the Bayesian and the Bhattacharyya distance RCE neural network classifiers is presented. The experiments are performed on synthetic and Brodatz textures. The introduction of the new classifier aims at obtaining a better performance in classifying non-stationary multi-texture images. The two classification schemes are assessed on their localized data representation regarding the ability of extracting non- stationary information from the image. Low-resolution data representation is used to reduce the instability produced with the search for a better trade-off between accuracy and spatial classification performances.

Paper Details

Date Published: 25 June 1999
PDF: 8 pages
Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351320
Show Author Affiliations
Emerson Prado Lopes, Univ. of Surrey (UK) and Federal Univ. of Rio de Janeiro (Brazil)


Published in SPIE Proceedings Vol. 3816:
Mathematical Modeling, Bayesian Estimation, and Inverse Problems
Françoise J. Prêteux; Ali Mohammad-Djafari; Edward R. Dougherty, Editor(s)

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