Evolving optimal histogram parameters for object recognition

John A. Rieffel; Christopher M. DiLeo; Bruce A. Maxwell

doi:10.1117/12.360298

26 August 1999 Evolving optimal histogram parameters for object recognition

John A. Rieffel, Christopher M. DiLeo, Bruce A. Maxwell

Proceedings Volume 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision; (1999) https://doi.org/10.1117/12.360298
Event: Photonics East '99, 1999, Boston, MA, United States

Abstract

3D color histograms are introduced as an effective means of object recognition. No globally optimal set of color histogram parameters is known, and the choice of data-set specific parameters is far from obvious due to the size of the search space involved. Evolution Strategies (ES), a form of Evolutionary Computation, are introduced as a method of optimizing histogram parameters specific to a known data set. An ES is implemented on a 22-object, 110 image database, and a 93 percent recognition rate achieved, a significant improvement over the 86 percent recognition rate of standard histogram axes. The results demonstrate the efficacy of ES and underscore the importance of the assumptions that histogram-based recognition methods are built upon.

Citation Download Citation

John A. Rieffel, Christopher M. DiLeo, and Bruce A. Maxwell "Evolving optimal histogram parameters for object recognition", Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); https://doi.org/10.1117/12.360298

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