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Proceedings Paper

Unsupervised Feature Selection For Object Recognition
Author(s): Jakub Segen
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Paper Abstract

A new method for selecting features for object recognition based on training data is proposed. This method avoids overspecifying or selecting too many features by using the criterion of minimal representation, which penalizes the representation complexity of features. The presented approach can be used to search for high level structural features such as relations or production rules.

Paper Details

Date Published: 23 May 1983
PDF: 4 pages
Proc. SPIE 0360, Robotics and Industrial Inspection, (23 May 1983); doi: 10.1117/12.934094
Show Author Affiliations
Jakub Segen, Bell Laboratories (United States)


Published in SPIE Proceedings Vol. 0360:
Robotics and Industrial Inspection
David P. Casasent, Editor(s)

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