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Proceedings Paper

Automatic feature extraction in a noniteratively learned pattern recognizer
Author(s): Chia-Lun John Hu
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Paper Abstract

After a frequency-domain-preprocessing of any standard 2D images, the standard patterns to be learned can generally be presented by N-D analog vectors. When these vectors satisfy the most general separability condition, the positive linear independency (or PLI) condition, these vectors can definitely be learned by a one-layered, hard-limited perceptron in a noniterative manner. From this noniterative learning scheme, an automatic feature extraction and automatic feature competition method can be derived which will enhance significantly the robustness in the recognition mode. This paper reports the theoretical analysis of this novel feature extraction scheme and the experimental result demonstrating the robustness of recognition and the speed of learning of this scheme. Comparison of this novel feature extraction scheme with Karhunen-Loeve scheme and the SVD (singular value decomposition) scheme is discussed in detail.

Paper Details

Date Published: 4 April 1997
PDF: 6 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271488
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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