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. criterion of the classification algorithm to select the optimal feature subset. Wrapper methods can provide more accurate solutions than filter methods [5], but in general are more computationally expensive. We present a new wrapper method to reduce the dimensions of both input and feature space of an SVM. An alternative approach for speeding-up SVM classification has been proposed in [7] by reducing the number of support vectors. Feature reduction is a generic tool that can be applied to any classification problem. When dealing with a specific classification task we can use prior knowledge about the type of data to speed-up classification.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2001
- Accession Number
- ADA458821
Entities
People
- Bernd Heisele
- Sayan Mukherjee
- Thomas Serre
- Tomaso Poggio
Organizations
- Massachusetts Institute of Technology