IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
Using random subspace to combine multiple features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
The Selective Random Subspace Predictor for Traffic Flow Forecasting
IEEE Transactions on Intelligent Transportation Systems
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Detecting objects (e.g., people, faces) from images is an important problem in many applications of machine learning and computer vision. Most past work has concentrated on the issues of feature extraction and classification, where less attention has been paid to the selection and manipulation of features. In this paper, the method of ensembles of feature subspaces is applied to construct multiple classifiers for object detection. Specifically, random subspace with sum rule and principal component analysis (PCA) are taken as a case study. Individual support vector machine classifiers are constructed using randomly selected eigenvectors from PCA spaces. Then sum rule is adopted to combine the prediction of individual classifiers. The performance of the proposed method is tested on two applications: pedestrian detection and face detection, which show promising results. We also compare sum rule with majority voting in their performance for weighting individual classifiers, and find that sum rule is superior.