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Boosted Projection: An Ensemble of Transformation Models

  • Ricardo Barbosa Kloss
  • Artur Jordão
  • William Robson Schwartz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

Computer vision problems usually suffer from a very high dimensionality, which can make it hard to learn classifiers. A way to overcome this problem is to reduce the dimensionality of the input. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. We conducted experiments in two important computer vision tasks: pedestrian detection and image classification. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares.

Keywords

Dimensionality reduction Machine learning Ensemble Partial Least Squares Computer vision 

Notes

Acknowledgments

The authors would like to thank the Brazilian National Research Council – CNPq, the Minas Gerais Research Foundation – FAPEMIG (Grants APQ-00567-14 and PPM-00540-17) and the Coordination for the Improvement of Higher Education Personnel – CAPES (DeepEyes Project). The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan X GPU used for this research.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Smart Surveillance Interest Group, Department of Computer ScienceUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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