Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
Abstract
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.
Cite
Text
Heisele et al. "Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990919Markdown
[Heisele et al. "Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/heisele2001cvpr-feature/) doi:10.1109/CVPR.2001.990919BibTeX
@inproceedings{heisele2001cvpr-feature,
title = {{Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images}},
author = {Heisele, Bernd and Serre, Thomas and Mukherjee, Sayan and Poggio, Tomaso A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {II:18-24},
doi = {10.1109/CVPR.2001.990919},
url = {https://mlanthology.org/cvpr/2001/heisele2001cvpr-feature/}
}