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26 August 1999 Evolving optimal histogram parameters for object recognition
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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