Unsupervised Feature Selection For Object Recognition

Jakub Segen

doi:10.1117/12.934094

23 May 1983 Unsupervised Feature Selection For Object Recognition

Jakub Segen

Proceedings Volume 0360, Robotics and Industrial Inspection; (1983) https://doi.org/10.1117/12.934094
Event: 26th Annual Technical Symposium, 1982, San Diego, United States

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.

Citation Download Citation

Jakub Segen "Unsupervised Feature Selection For Object Recognition", Proc. SPIE 0360, Robotics and Industrial Inspection, (23 May 1983); https://doi.org/10.1117/12.934094

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available