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CVPR
2001
IEEE

Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images

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Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.
Bernd Heisele, Thomas Serre, Sayan Mukherjee, Toma
Added 12 Oct 2009
Updated 08 Jul 2010
Type Conference
Year 2001
Where CVPR
Authors Bernd Heisele, Thomas Serre, Sayan Mukherjee, Tomaso Poggio
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