Underwater Live Fish Recognition by Deep Learning

  • Abdelouahid Ben Tamou
  • Abdesslam BenzinouEmail author
  • Kamal Nasreddine
  • Lahoucine Ballihi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


Recently, underwater videos have gained great interest by marine ecologists for studying fish populations. Actually, this technique produces large amount of visual data and does not affect fish behavior. However, visual processing and analyzing of the recorded data can be subjective, time consuming and costly. We propose in this paper to use the convolutional neural network AlexNet with transfer learning for automatic fish species classification. We extract features from foreground fish images of the available underwater dataset using the pretrained AlexNet network either with or without fine-tunig. For classification, we use a linear SVM classifier. The experiment results demonstrate the effectiveness of the proposed approach on the Fish Recognition Ground-Truth dataset. We achieve an accuracy of 99.45%.


Deep learning Transfer learning Convolutional neural network Pretrained model AlexNet Fish recognition 



The authors would like to thank the Région Bretagne for financial support.


  1. 1.
    Spampinato, C., Giordano, D., Di Salvo, R., Chen-Burger, Y.H.J., Fisher, R.B., Nadarajan, G.: Automatic fish classification for underwater species behavior understanding. In: Proceedings of the First ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, pp. 45–50. ACM, October 2010Google Scholar
  2. 2.
    Li, X., Shang, M., Qin, H., Chen, L.: Fast accurate fish detection and recognition of underwater images with fast R-CNN. In: OCEANS 2015 MTS/IEEE Washington, pp. 1–5. IEEE, October 2015Google Scholar
  3. 3.
    Qin, H., Li, X., Yang, Z., Shang, M.: When underwater imagery analysis meets deep learning: a solution at the age of big visual data. In: OCEANS 2015 MTS/IEEE Washington, pp. 1–5. IEEE, October 2015Google Scholar
  4. 4.
    Qin, H., Li, X., Liang, J., Peng, Y., Zhang, C.: Deepfish: accurate underwater live fish recognition with a deep architecture. Neurocomputing 187, 49–58 (2015)CrossRefGoogle Scholar
  5. 5.
    Sun, X., Shi, J., Dong, J., Wang, X.: Fish recognition from low-resolution underwater images. In: International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 471–476. IEEE, October 2016Google Scholar
  6. 6.
    Chuang, M.C., Hwang, J.N., Williams, K.: Automatic fish segmentation and recognition for trawl-based cameras. In: Computer Vision and Pattern Recognition in Environmental Informatics, pp. 79–106. IGI Global (2016)Google Scholar
  7. 7.
    Schettini, R., Corchs, S.: Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J. Adv. Signal Process. 2010(1), 746052 (2010)CrossRefGoogle Scholar
  8. 8.
    Chambah, M., Semani, D., Renouf, A., Courtellemont, P., Rizzi, A.: Underwater color constancy: enhancement of automatic live fish recognition. In: Color Imaging IX: Processing, Hardcopy, and Applications. International Society for Optics and Photonics, Vol. 5293, pp. 157–169, December 2003Google Scholar
  9. 9.
    LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  11. 11.
    Choi, S: Fish identification in underwater video with deep convolutional neural network: SNUMedinfo at LifeCLEF fish task 2015. In: CLEF (2015). Working NotesGoogle Scholar
  12. 12.
    Joly, A., et al.: LifeCLEF 2015: multimedia life species identification challenges. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 462–483. Springer, Cham (2015). Scholar
  13. 13.
    Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)CrossRefGoogle Scholar
  14. 14.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  15. 15.
    Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lin, M., Chen, Q., Yan, S.: Network in network (2013). arXiv preprint arXiv:1312.4400
  17. 17.
    Salman, A., Jalal, A., Shafait, F., Mian, A., Shortis, M., Seager, J., Harvey, E.: Fish species classification in unconstrained underwater environments based on deep learning. Limnol. Oceanogr. Methods 14(9), 570–585 (2016)CrossRefGoogle Scholar
  18. 18.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
  20. 20.
    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
  21. 21.
    Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 248–255. IEEE, June 2009Google Scholar
  22. 22.
    Qin, H., Peng, Y., Li, X.: Foreground extraction of underwater videos via sparse and low-rank matrix decomposition. In: 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), pp. 65–72. IEEE, August 2014Google Scholar
  23. 23.
    Boom, B.J., Huang, P.X., He, J., Fisher, R.B.: Supporting ground-truth annotation of image datasets using clustering. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1542–1545. IEEE, November 2012Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Abdelouahid Ben Tamou
    • 1
    • 2
  • Abdesslam Benzinou
    • 1
    Email author
  • Kamal Nasreddine
    • 1
  • Lahoucine Ballihi
    • 2
  1. 1.Univ Bretagne Loire, ENIB, UMR CNRS 6285 LabSTICCBrestFrance
  2. 2.LRIT-CNRST URAC 29, Mohammed V University In Rabat, FSRRabatMorocco

Personalised recommendations