Machine Learning
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Representations and Feature Selection for Multimedia Database Search
IEEE Transactions on Knowledge and Data Engineering
An Information Criterion for Variable Selection in Support Vector Machines
The Journal of Machine Learning Research
Random projections for face detection under resource constraints
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper, we present an improved feature reduction method in input and feature spaces for classification using support vector machines (SVMs). In the input space, we select a subset of input features by ranking their contributions to the decision function. In the feature space, features are ranked according to the weighted support vector in each dimension. By applying feature reduction in both input and feature spaces, we develop a fast non-linear SVM without a significant loss in performance. We have tested the proposed method on the detection of face, person, and car. Subsets of features are chosen from pixel values for face detection and from Haar wavelet features for person and car detection. The experimental results show that the proposed feature reduction method works successfully. In fact, our method performs better than the methods of using all the features and the Fisher's features in the detection of person and car. We also gain the advantage of speed.