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Proceedings Paper

Morphological feature-set optimization using the genetic algorithm
Author(s): John M. Trenkle; Steven G. Schlosser; Robert C. Vogt
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Paper Abstract

This paper is an investigation into the use of genetic algorithm techniques for doing optimal feature set selection in order to discriminate large sets of characters. Human experts defined a set of over 900 features from many different classes which could be used to help discriminate different characters from a chosen character set. Each of the features was assigned a cost, based on the average amount of CPU time necessary to compute it for a typical character. The goal of the task was to find the subset of features which produced the best trade-off between recognition accuracy and computational cost. The authors were able to show that by using all of the features or even major classes of them, high rates of discrimination accuracy for a printed character set (above 98% correct, first choice) could be obtained. Application of the genetic algorithm to selected subsets of characters and features demonstrated the ability of the method to significantly reduce the computational cost of the classification system and maintain or increase accuracy from the case where a complete set of features was used.

Paper Details

Date Published: 1 July 1991
PDF: 12 pages
Proc. SPIE 1568, Image Algebra and Morphological Image Processing II, (1 July 1991); doi: 10.1117/12.46117
Show Author Affiliations
John M. Trenkle, Environmental Research Institute of Michigan (United States)
Steven G. Schlosser, Environmental Research Institute of Michigan (United States)
Robert C. Vogt, Environmental Research Institute of Michigan (United States)


Published in SPIE Proceedings Vol. 1568:
Image Algebra and Morphological Image Processing II
Paul D. Gader; Edward R. Dougherty, Editor(s)

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