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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 321–330Cite as

Pollen Grain Recognition Using Deep Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

Pollen identification helps forensic scientists solve elusive crimes, provides data for climate-change modelers, and even hints at potential sites for petroleum exploration. Despite its wide range of applications, most pollen identification is still done by time-consuming visual inspection by well-trained experts. Although partial automation is currently available, automatic pollen identification remains an open problem. Current pollen-classification methods use pre-designed features of texture and contours, which may not be sufficiently distinctive. Instead of using pre-designed features, our pollen-recognition method learns both features and classifier from training data under the deep-learning framework. To further enhance our network’s classification ability, we use transfer learning to leverage knowledge from networks that have been pre-trained on large datasets of images. Our method achieved \(\approx \)94% classification rate on a dataset of 30 pollen types. These rates are among the highest obtained in this problem.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Florida Institute of Technology, Melbourne, Florida, USA

    Amar Daood

  2. Department of Computer Sciences and Cybersecurity, Florida Institute of Technology, Melbourne, Florida, USA

    Eraldo Ribeiro

  3. Department of Biological Sciences, Florida Institute of Technology, Melbourne, Florida, USA

    Mark Bush

Authors
  1. Amar Daood
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  2. Eraldo Ribeiro
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  3. Mark Bush
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Corresponding author

Correspondence to Amar Daood .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Cite this paper

Daood, A., Ribeiro, E., Bush, M. (2016). Pollen Grain Recognition Using Deep Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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