An efficient feature selection method for object detection

Authors:
Duy-Dinh Le;Shin'ichi Satoh
Affiliations:
The Graduate University for Advanced Studies, Kanagawa, Japan;The Graduate University for Advanced Studies, Kanagawa, Japan
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

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Abstract

We propose a simple yet efficient feature-selection method — based on principle component analysis (PCA) — for SVM-based classifiers. The idea is to select features whose corresponding axes are closest to the principle components computed from a data distribution by PCA. Experimental results show that our proposed method reduces dimensionality similar to PCA, but maintains the original measurement meanings while decreasing the computation time significantly.