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Open-Set Plankton Recognition Using Similarity Learning

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13598))

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Abstract

Automatic plankton recognition provides new possibilities to study plankton populations and various environmental aspects related to them. Most of the existing recognition methods focus on individual datasets with a known set of classes limiting their wider applicability. Automated plankton imaging instruments capture images of unknown particles and the class (plankton species) composition varies between geographical regions and ecosystems. This calls for an open-set recognition method that is able to reject images from unknown classes and can be easily generalized to new classes. In this paper, we show that a flexible model capable of high classification accuracy can be obtained by utilizing similarity learning and a gallery set of known plankton species. The model is shown to generalize well for new plankton classes added in the gallery set without retraining the model. This provides a good basis for the wider utilization of plankton recognition methods in aquatic research.

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Notes

  1. 1.

    http://doi.org/10.23728/b2share.abf913e5a6ad47e6baa273ae0ed6617a.

References

  1. Bendale, A., Boult, T.: Towards open set deep networks. In: Conference on Computer Vision and Pattern Recognition, pp. 1563–1572 (2016)

    Google Scholar 

  2. Bueno, G., et al.: Automated diatom classification (part A): handcrafted feature approaches. Appl. Sci. 7(8), 753 (2017)

    Article  Google Scholar 

  3. Bureš, J., Eerola, T., Lensu, L., Kälviäinen, H., Zemčík, P.: Plankton recognition in images with varying size. In: International Conference on Pattern Recognition Workshops, pp. 110–120 (2021)

    Google Scholar 

  4. Dai, J., Wang, R., Zheng, H., Ji, G., Qiao, X.: ZooplanktoNet: deep convolutional network for zooplankton classification. In: OCEANS Conference, pp. 1–6 (2016)

    Google Scholar 

  5. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  6. Dubey, S.R.: A decade survey of content based image retrieval using deep learning. IEEE Trans. Circuits Syst. Video Technol. 32(5), 2687–2704 (2021)

    Article  Google Scholar 

  7. Geng, C., Huang, S.J., Chen, S.: Recent advances in open set recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3614–3631 (2020)

    Google Scholar 

  8. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  9. Henrichs, D.W., Anglès, S., Gaonkar, C.C., Campbell, L.: Application of a convolutional neural network to improve automated early warning of harmful algal blooms. Environ. Sci. Pollut. Res. 28(22), 28544–28555 (2021)

    Article  Google Scholar 

  10. Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: International Workshop on Similarity-Based Pattern Recognition, pp. 84–92 (2015)

    Google Scholar 

  11. Kraft, K., et al.: Towards operational phytoplankton recognition with automated high-throughput imaging, near real-time data processing, and convolutional neural networks. Front. Marine Sci. 9 (2022)

    Google Scholar 

  12. Lumini, A., Nanni, L.: Deep learning and transfer learning features for plankton classification. Econ. Inform. 51, 33–43 (2019)

    Article  Google Scholar 

  13. Mitra, R., et al.: Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance. Mar. Micropaleontol. 147, 16–24 (2019)

    Article  Google Scholar 

  14. Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No fuss distance metric learning using proxies. In: International Conference on Computer Vision, pp. 360–368 (2017)

    Google Scholar 

  15. Nepovinnykh, E., Eerola, T., Kalviainen, H.: Siamese network based pelage pattern matching for ringed seal re-identification. In: Winter Conference on Applications of Computer Vision Workshops, pp. 25–34 (2020)

    Google Scholar 

  16. Ni, X., Huttunen, H.: Vehicle attribute recognition by appearance: computer vision methods for vehicle type, make and model classification. J. Sig. Process. Syst. 93(4), 357–368 (2021)

    Article  Google Scholar 

  17. Olson, R., Sosik, H.: A submersible imaging-in-flow instrument to analyze nano-and microplankton: imaging flowcytobot. Limnol. Oceanogr. Methods 5(6), 195–203 (2007)

    Article  Google Scholar 

  18. Orenstein, E., Beijbom, O.: Transfer learning and deep feature extraction for planktonic image data sets. In: Winter Conference on Applications of Computer Vision, pp. 1082–1088 (2017)

    Google Scholar 

  19. Pu, Y., Feng, Z., Wang, Z., Yang, Z., Li, J.: Anomaly detection for in situ marine plankton images. In: International Conference on Computer Vision, pp. 3661–3671 (2021)

    Google Scholar 

  20. Radenović, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655–1668 (2018)

    Article  Google Scholar 

  21. Teigen, A.L., Saad, A., Stahl, A.: Leveraging similarity metrics to in-situ discover planktonic interspecies variations or mutations. In: Global Oceans 2020: Singapore-US Gulf Coast, pp. 1–8 (2020)

    Google Scholar 

  22. Walker, J., Orenstein, E.: Improving rare-class recognition of marine plankton with hard negative mining. In: International Conference on Computer Vision, pp. 3672–3682 (2021)

    Google Scholar 

  23. Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 2872–2893 (2021)

    Article  Google Scholar 

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Acknowledgements

The research was carried out in the FASTVISION and FASTVISION-plus projects funded by the Academy of Finland (Decision numbers 321980, 321991, 339612, and 339355).

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Authors and Affiliations

  1. Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Lappeenranta, Finland

    Ola Badreldeen Bdawy Mohamed, Tuomas Eerola, Lasse Lensu & Heikki Kälviäinen

  2. Finnish Environment Institute, Marine Research Centre, Helsinki, Finland

    Kaisa Kraft

Authors
  1. Ola Badreldeen Bdawy Mohamed
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  2. Tuomas Eerola
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  3. Kaisa Kraft
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  4. Lasse Lensu
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  5. Heikki Kälviäinen
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Corresponding author

Correspondence to Tuomas Eerola .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Badreldeen Bdawy Mohamed, O., Eerola, T., Kraft, K., Lensu, L., Kälviäinen, H. (2022). Open-Set Plankton Recognition Using Similarity Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-20713-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20712-9

  • Online ISBN: 978-3-031-20713-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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