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Identification of Saimaa Ringed Seal Individuals Using Transfer Learning

  • Ekaterina Nepovinnykh
  • Tuomas Eerola
  • Heikki Kälviäinen
  • Gleb Radchenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11182)

Abstract

The conservation efforts of the endangered Saimaa ringed seal depend on the ability to reliably estimate the population size and to track individuals. Wildlife photo-identification has been successfully utilized in monitoring for various species. Traditionally, the collected images have been analyzed by biologists. However, due to the rapid increase in the amount of image data, there is a demand for automated methods. Ringed seals have pelage patterns that are unique to each seal enabling the individual identification. In this work, two methods of Saimaa ringed seal identification based on transfer learning are proposed. The first method involves retraining of an existing convolutional neural network (CNN). The second method uses the CNN trained for image classification to extract features which are then used to train a Support Vector Machine (SVM) classifier. Both approaches show over 90% identification accuracy on challenging image data, the SVM based method being slightly better.

Keywords

Animal biometrics Saimaa ringed seals Convolutional neural networks Transfer learning Identification Image segmentation 

Notes

Acknowledgements

The authors would like to thank Meeri Koivuniemi, Miina Auttila, Riikka Levä-nen, Marja Niemi, and Mervi Kunnasranta from Department of Environmental and Biological Sciences at University of Eastern Finland for providing the database for the experiments and expert knowledge for identifying the individuals.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ekaterina Nepovinnykh
    • 1
    • 2
  • Tuomas Eerola
    • 1
  • Heikki Kälviäinen
    • 1
  • Gleb Radchenko
    • 2
  1. 1.Machine Vision and Pattern Recognition Laboratory, Department of Computational and Process Engineering, School of Engineering ScienceLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.School of Electrical Engineering and Computer ScienceSouth Ural State UniversityChelyabinskRussian Federation

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