An Efficient Feature Selection Method For Object Detection

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概要

• 論文の詳細を見る

• Feature selection is one of the important tasks in many object detection systems because it can improve performance and speed of classifiers. In this paper, we present 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 principle components computed from 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.

• 社団法人電子情報通信学会の論文
• 2005-06-10

著者

• LE Duy

  The Graduate University for Advanced Studies

• Satoh Shinichi

  The Graduate University For Advanced Studies:national Institute Of Informatics

関連論文

• LI-008 Feature Selection By AdaBoost For SVM-Based Face Detection
• An Efficient Feature Selection Method For Object Detection
• An Efficient Feature Selection Method For Object Detection

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