Secondary classification using key features

Venu Govindaraju; Zhixin Shi; A. Teredesai

doi:10.1117/12.410846

21 December 2000 Secondary classification using key features

Venu Govindaraju, Zhixin Shi, A. Teredesai

Proceedings Volume 4307, Document Recognition and Retrieval VIII; (2000) https://doi.org/10.1117/12.410846
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

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.

Citation Download Citation

Venu Govindaraju, Zhixin Shi, and A. Teredesai "Secondary classification using key features", Proc. SPIE 4307, Document Recognition and Retrieval VIII, (21 December 2000); https://doi.org/10.1117/12.410846

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