Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast Support Vector Machine Classification using linear SVMs
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
Over-complete wavelet approximation of a support vector machine for efficient classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of similar architectures with more powerful (strong) classifiers has become the subject of current research. In this paper, we focus on greedy strategies recently proposed in the literature that allow to learn sparse Support Vector Machines (SVMs) without the need to train full SVMs beforehand. We show (i) that asymmetric data sets that are typical for object detection scenarios can be successfully handled, and (ii) that the complementary training of two sparse SVMs leads to sequential two-stage classifiers that slightly outperform a full SVM, but only need about 10% kernel evaluations for classifying a pattern.