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2020 | OriginalPaper | Chapter

Branch-and-Bound Search for Training Cascades of Classifiers

Authors : Dariusz Sychel, Przemysław Klęsk, Aneta Bera

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

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Abstract

We propose a general algorithm that treats cascade training as a tree search process working according to the branch-and-bound technique. The algorithm allows to reduce the expected number of features used by an operating cascade—a key quantity we focus on in the paper. While searching, we observe suitable lower bounds on partial expectations and prune tree branches that cannot improve the best-so-far result. Both exact and approximate variants of the approach are formulated. Experiments pertain to cascades trained to be face or letter detectors with Haar-like features or Zernike moments being the input information, respectively. Results confirm shorter operating times of cascades obtained owing to the reduction in the number of extracted features.

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Metadata
Title
Branch-and-Bound Search for Training Cascades of Classifiers
Authors
Dariusz Sychel
Przemysław Klęsk
Aneta Bera
Copyright Year
2020
Book
Computational Science – ICCS 2020

Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-50423-6_2