Journal
PATTERN RECOGNITION
Volume 36, Issue 9, Pages 2007-2017Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0031-3203(03)00062-1
Keywords
face detection; object detection; feature reduction; hierarchical classification; support vector machines
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We present a two-step method to speed-up object detection systems in computer vision that use support vector machines as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level, a simple and fast linear 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. We propose a new method for automatically building and training a hierarchy of classifiers. In the second step we apply feature reduction to the top level classifier by choosing relevant image features according to a measure derived from statistical learning theory. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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