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Proceedings Paper

Hierarchical neural net with pyramid data structures for region labeling of images
Author(s): David P. Rosten; Patrick Wingkee Yuen; Bobby R. Hunt
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Paper Abstract

In practical pattern recognition problems, one-shot classifiers such as single feedforward neural networks trained by back-propagation may operate inefficiently in a complex pattern space and/or have unstable trained configurations. An alternative is a decision tree classifier. The authors report on the design, training, and accuracy of a hierarchical classifier implementing neural nets. Each nonterminal node is a separate feedforward neural network and is neither restricted to binary decisions nor limited to using only one feature to make those decisions. The features are pyramid data structures: identical texture parameters calculated across three different image resolutions about the training sites. In this application, results show a twenty percent relative increase in accuracy over the monolithic classifier.

Paper Details

Date Published: 1 August 1991
PDF: 10 pages
Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); doi: 10.1117/12.46477
Show Author Affiliations
David P. Rosten, Univ. of Arizona (United States)
Patrick Wingkee Yuen, Univ. of Arizona (United States)
Bobby R. Hunt, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 1472:
Image Understanding and the Man-Machine Interface III
Eamon B. Barrett; James J. Pearson, Editor(s)

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