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German Conference on Pattern Recognition

GCPR 2018: Pattern Recognition pp 391–404Cite as

Low-Shot Learning of Plankton Categories

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

Abstract

The size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few examples. We employ the recently introduced weight imprinting technique in order to use the available training data to train accurate classifiers in absence of enough examples for some classes.

The model architecture used in this work succeeds in the identification of plankton using machine learning with its unique challenges, i.e. a limited number of training examples and a severely skewed class size distribution. Weight imprinting enables a neural network to recognize small classes immediately without re-training. This permits the mining of examples for novel classes.

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Acknowledgements

Rainer Kiko was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Centre (SFB) 754 “Climate-Biogeochemistry Interactions in the Tropical Ocean.” Rainer Kiko, Reinhard Koch and Simon-Martin Schröder were furthermore supported by grants CP1650 and CP1733 of the Cluster of Excellence 80 “The Future Ocean.” “The Future Ocean” is funded within the framework of the Excellence Initiative by the Deutsche Forschungsgemeinschaft (DFG) on behalf of the German federal and state governments. Jean-Olivier Irisson was supported by CNRS LEFE-MANU through project DL-PIC.

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Authors and Affiliations

  1. Department of Computer Science, Kiel University, Kiel, Germany

    Simon-Martin Schröder & Reinhard Koch

  2. GEOMAR Helmholtz-Centre for Ocean Research, Kiel, Germany

    Rainer Kiko

  3. Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefanche, LOV, Villefranche-sur-mer, France

    Jean-Olivier Irisson

Authors
  1. Simon-Martin Schröder
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  2. Rainer Kiko
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  3. Jean-Olivier Irisson
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  4. Reinhard Koch
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Corresponding author

Correspondence to Simon-Martin Schröder .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Schröder, SM., Kiko, R., Irisson, JO., Koch, R. (2019). Low-Shot Learning of Plankton Categories. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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