Efficient Boosted Weak Classifiers for Object Detection

  • Xiaopeng Hong
  • Guoying Zhao
  • Haoyu Ren
  • Xilin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

This paper accelerates boosted nonlinear weak classifiers in boosting framework for object detection. Although conventional nonlinear classifiers are usually more powerful than linear ones, few existing methods integrate them into boosting framework as weak classifiers owing to the highly computational cost. To address this problem, this paper proposes a novel nonlinear weak classifier named Partition Vector weak Classifier (PVC), which is based on the histogram intersection kernel functions of the feature vector with respect to a set of pre-defined Partition Vectors (PVs). A three-step algorithm is derived from the kernel trick for efficient weak learning. The obtained PVCs are further accelerated via building a look-up table. Experimental results in the detection tasks for multiple classes of objects show that boosted PVCs significantly improves both learning and evaluation efficiency of nonlinear SVMs to the level of boosted linear classifiers, without losing any of the high discriminative power.

Keywords

Support Vector Machine Object Detection Linear Classifier Weak Classifier Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaopeng Hong
    • 1
  • Guoying Zhao
    • 1
  • Haoyu Ren
    • 2
  • Xilin Chen
    • 2
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OuluFinland
  2. 2.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing TechnologyCASP.R. China

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