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Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification

  • Rezaul Karim
  • Martin Bergtholdt
  • Jörg Kappes
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of similar architectures with more powerful (strong) classifiers has become the subject of current research. In this paper, we focus on greedy strategies recently proposed in the literature that allow to learn sparse Support Vector Machines (SVMs) without the need to train full SVMs beforehand. We show (i) that asymmetric data sets that are typical for object detection scenarios can be successfully handled, and (ii) that the complementary training of two sparse SVMs leads to sequential two-stage classifiers that slightly outperform a full SVM, but only need about 10% kernel evaluations for classifying a pattern.

Keywords

Support Vector Machine Support Vector Face Detection Greedy Search Greedy Strategy 
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 2007

Authors and Affiliations

  • Rezaul Karim
    • 1
  • Martin Bergtholdt
    • 1
  • Jörg Kappes
    • 1
  • Christoph Schnörr
    • 1
  1. 1.University of Mannheim, Dept. Math & CS – CVGPR Group 

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