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Proceedings Paper

Secondary classification using key features
Author(s): Venu Govindaraju; Zhixin Shi; A. Teredesai
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Paper Abstract

A new multiple level classification method is introduced. With an available feature set, classification can be done in several steps. After first step of the classification using the full feature set, the high confidence recognition result will lead to an end of the recognition process. Otherwise a secondary classification designed using partial feature set and the information available from earlier classification step will help classify the input further. In comparison with the existing methods, our method is aimed for increasing recognition accuracy and reliability. A feature selection mechanism with help of genetic algorithms is employed to select important features that provide maximum separability between classes under consideration. These features are then used to get a sharper decision on fewer classes in the secondary classification. The full feature set is still used in earlier classification to retain complete information. There are no features dumped as they would be in feature selection methods described in most related publications.

Paper Details

Date Published: 21 December 2000
PDF: 8 pages
Proc. SPIE 4307, Document Recognition and Retrieval VIII, (21 December 2000); doi: 10.1117/12.410846
Show Author Affiliations
Venu Govindaraju, SUNY/Buffalo (United States)
Zhixin Shi, SUNY/Buffalo (United States)
A. Teredesai, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 4307:
Document Recognition and Retrieval VIII
Paul B. Kantor; Daniel P. Lopresti; Jiangying Zhou, Editor(s)

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