1 August 1991 Hierarchical neural net with pyramid data structures for region labeling of images
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David P. Rosten, David P. Rosten, Patrick Wingkee Yuen, Patrick Wingkee Yuen, Bobby R. Hunt, 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); doi: 10.1117/12.46477; https://doi.org/10.1117/12.46477

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