Abstract
Plankton is one of the most abundant and diverse class of microscopic organisms inhabiting the Earth. Their enormous intra- and inter-species genetic and phenotypic diversity, coupled with the limited amount of large survey data, makes it hard to obtain a complete representation of this important class of organisms. Hence, the classification accuracy of novel supervised machine learning algorithms is bound to be limited by the incompleteness of the training data. In this work we introduce an efficient pipeline centered around a novel anomaly detection algorithm to discover and classify new plankton species, in situ, with the aim of automatically populating a plankton database in an unsupervised fashion. Our pipeline utilizes the concept of anomaly detection to separate a novel species from the ones contained in an initial existing database. Our results show that the implemented algorithm outperforms four state-of-the-art methods for outlier detection on the plankton dataset used in our analysis. Finally, using a leave-one-out approach, we prove that our pipeline is able to identify unknown plankton species with high-accuracy.
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References
Behrenfeld, M.J., et al.: Biospheric primary production during an ENSO transition. Science 291(5513), 2594–2597 (2001)
Blaschko, M.B., et al.: Automatic in situ identification of plankton. In: 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION 2005), vol. 1, pp. 79–86 (2005). https://doi.org/10.1109/ACVMOT.2005.29
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000). https://doi.org/10.1145/335191.335388
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Cheng, K., Cheng, X., Wang, Y., Bi, H., Benfield, M.C.: Enhanced convolutional neural network for plankton identification and enumeration. PLOS ONE 14(7), 1–17 (2019). https://doi.org/10.1371/journal.pone.0219570
Fossum, T.O., et al.: Toward adaptive robotic sampling of phytoplankton in the coastal ocean. Sci. Rob. 4(27), eaav3041 (2019). https://doi.org/10.1126/scirobotics.aav3041
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314
Huang, Z., Leng, J.: Analysis of hu’s moment invariants on image scaling and rotation, vol. 7, pp. V7–476 (2010). https://doi.org/10.1109/ICCET.2010.5485542
Hughes, A.J., et al.: Quanti.us: a tool for rapid, flexible, crowd-based annotation of images. Nature 15(8), 587–590 (2018). https://doi.org/10.1038/s41592-018-0069-0
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008). https://doi.org/10.1109/ICDM.2008.17
Lumini, A., Nanni, L.: Deep learning and transfer learning features for plankton classification. Ecol. Inf. 51, 33–43 (2019)
Pastore, V.P., Zimmerman, T., Biswas, S.K., Bianco, S.: Establishing the baseline for using plankton as biosensor. In: Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVII, vol. 10881, p. 108810H. International Society for Optics and Photonics (2019)
Pastore, V.P., Zimmerman, T.G., Biswas, S.K., Bianco, S.: Annotation-free learning of plankton for classification and anomaly detection. Sci. Rep. 10(1), 12142 (2020). https://doi.org/10.1038/s41598-020-68662-3
Rousseeuw, P., Driessen, K.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223 (1999). https://doi.org/10.1080/00401706.1999.10485670
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS 1999, pp. 582–588. MIT Press, Cambridge (1999)
Schröder, S.M., Kiko, R., Koch, R.: Morphocluster: efficient annotation of plankton images by clustering. Sensors 20(11), 3060 (2020)
Sosik, H.M., Olson, R.J.: Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr. Methods 5(6), 204–216 (2007)
Sournia, A., Chrdtiennot-Dinet, M.J., Ricard, M.: Marine phytoplankton: how many species in the world ocean? J. Plankton Res. 13(5), 1093–1099 (1991). https://doi.org/10.1093/plankt/13.5.1093
Yang, Z., Fang, T.: On the accuracy of image normalization by zernike moments. Image Vision Comput. 28(3), 403–413 (2010). https://doi.org/10.1016/j.imavis.2009.06.010
Zheng, H., Wang, R., Yu, Z., Wang, N., Gu, Z., Zheng, B.: Automatic plankton image classification combining multiple view features via multiple kernel learning. BMC Bioinf. 18(16), 570 (2017). https://doi.org/10.1186/s12859-017-1954-8
Zimmerman, T., Smith, B.: Lensless stereo microscopic imaging. In: ACM SIGGRAPH 2007: Emerging Technologies, SIGGRAPH 2007, p. 15 (2007). https://doi.org/10.1145/1278280.1278296
Acknowledgements
We thank all faculty and students in the National Science Foundation Center for Cellular Construction and the Machine Learning Genoa Center (MaLGa) for discussion and critical feedback on the general idea and pipeline. This material is partially based upon work supported by the National Science Foundation under Grant No. DBI-1548297.
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Pastore, V.P., Megiddo, N., Bianco, S. (2022). An Anomaly Detection Approach for Plankton Species Discovery. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_50
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