Hierarchical neural net with pyramid data structures for region labeling of images
David P. Rosten, Patrick Wingkee Yuen, Bobby R. Hunt · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
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.