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Proceedings Paper

Evolving optimal histogram parameters for object recognition
Author(s): John A. Rieffel; Christopher M. DiLeo; Bruce A. Maxwell
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Paper 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.

Paper Details

Date Published: 26 August 1999
PDF: 8 pages
Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); doi: 10.1117/12.360298
Show Author Affiliations
John A. Rieffel, Swarthmore College (United States)
Christopher M. DiLeo, Swarthmore College (United States)
Bruce A. Maxwell, Swarthmore College (United States)

Published in SPIE Proceedings Vol. 3837:
Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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