Hierarchical neural net with pyramid data structures for region        labeling of images

David P. Rosten; Patrick Wingkee Yuen; Bobby R. Hunt

doi:10.1117/12.46477

1 August 1991 Hierarchical neural net with pyramid data structures for region labeling of images

David P. Rosten, Patrick Wingkee Yuen, Bobby R. Hunt

Proceedings Volume 1472, Image Understanding and the Man-Machine Interface III; (1991) https://doi.org/10.1117/12.46477
Event: Orlando '91, 1991, Orlando, FL, United States

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.

Citation Download Citation

David P. Rosten, Patrick Wingkee Yuen, and Bobby R. Hunt "Hierarchical neural net with pyramid data structures for region labeling of images", Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); https://doi.org/10.1117/12.46477

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