Skip to main content
SpringerLink
Log in

Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

  • Conference paper
  • First Online:
Advanced Concepts for Intelligent Vision Systems (ACIVS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10016))

Abstract

In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://catlinseaviewsurvey.com/.

  2. 2.

    http://www.imageclef.org/lifeclef/2016/sea.

  3. 3.

    https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  4. 4.

    The overlap ratio is defined as \(OA= IS/US\) with IS the intersection surface and US the union surface.

References

  1. Mallet, D., Pelletier, D.: Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fish. Res. 154, 44–62 (2014)

    Article  Google Scholar 

  2. Boom, B.J., Huang, P.X., Beyan, C., et al.: Long-term underwater camera surveillance for monitoring and analysis of fish populations. In: VAIB12 (2012)

    Google Scholar 

  3. Fisher, R.B., Chen-Burger, Y.-H., Giordano, D., Hardman, L., Lin, F.-P. (eds.): Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data. ISRL, vol. 104. Springer, Heidelberg (2016)

    Google Scholar 

  4. Alsmadi, M.K.S., Omar, K.B., Noah, S.A., et al.: Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree. J. Comput. Sci. 6(10), 1088–1094 (2010)

    Article  Google Scholar 

  5. Rova, A., Mori, G., Dill, L.M.: One fish, two fish, butterfish, trumpeter: recognizing fish in underwater video. In: Machine Vision Applications, pp. 404–407 (2007)

    Google Scholar 

  6. Spampinato, C., Giordano, D., Di Salvo, R.: 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 (2010)

    Google Scholar 

  7. Hearst, M.A., Dumais, S.T., Osman, E., et al.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998). MLA

    Article  Google Scholar 

  8. Matai, J., Kastner, R., Cutter Jr., G.R.: Automated techniques for detection, recognition of fishes using computer vision algorithms. In: Williams, K., Rooper, C., Harms, J. (eds.) NOAA Technical Memorandum NMFS-F/SPO-121, Report of the National Marine Fisheries Service Automated Image Processing Workshop, 4–7 September 2010, Seattle, Washington (2010)

    Google Scholar 

  9. Shiau, Y.-H., Lin, S.-I., Chen, Y.-H., et al.: Fish observation, detection, recognition, verification in the real world. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p. 1 (2012)

    Google Scholar 

  10. Blanc, K., Lingrand, D., Precioso, F.: Fish species recognition from video using SVM classifier. In: Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data, pp. 1–6. ACM (2014)

    Google Scholar 

  11. Zhu, Q., Yeh, M.-C., Cheng, K.-T., et al.: Fast human detection using a cascade of histograms of oriented gradients. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1491–1498. IEEE (2006)

    Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893. IEEE (2005)

    Google Scholar 

  13. Pasquet, J., Chaumont, M., Subsol, G.: Comparaison de la segmentation pixel et segmentation objet pour la détection d’objets multiples et variables dans des images. In: CORESA: COmpression et REprésentation des Signaux Audiovisuels, Reims (2014). (in French)

    Google Scholar 

  14. Das, S., Mirnalinee, T.T., Varghese, K.: Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE Trans. Geosci. Remote Sens. 49(10), 3906–3931 (2011)

    Article  Google Scholar 

  15. Sun, X., Wang, H., Fu, K.: Automatic detection of geospatial objects using taxonomic semantics. IEEE Geosci. Remote Sens. Lett. 7(1), 23–27 (2010)

    Article  Google Scholar 

  16. Zhang, W., Sun, X., Fu, K., et al.: Object detection in high-resolution remote sensing images using rotation invariant parts based model. IEEE Geosci. Remote Sens. Lett. 11(1), 74–78 (2014)

    Article  Google Scholar 

  17. Zhang, W., Sun, X., Wang, H., et al.: A generic discriminative part-based model for geospatial object detection in optical remote sensing images. ISPRS J. Photogrammetry Remote Sens. 99, 30–44 (2015)

    Article  Google Scholar 

  18. 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 

  19. Atkinson, P.M., Tatnall, A.R.L.: Introduction neural networks in remote sensing. Int. J. Remote Sens. 18(4), 699–709 (1997)

    Article  Google Scholar 

  20. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  21. Lecun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  22. Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  23. Joly, A., et al.: LifeCLEF 2015: multimedia life species identification challenges. In: Mothe, J., Savoy, J., Kamps, J., Pinel-Sauvagnat, K., Jones, G.J.F., SanJuan, E., Cappellato, L., Ferro, N. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 462–483. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24027-5_46

    Chapter  Google Scholar 

Download references

Acknowledgement

This work has been carried out thanks to the support of the LabEx NUMEV project (no ANR-10-LABX-20) funded by the “Investissements d’Avenir” French Government program, managed by the French National Research Agency (ANR). We thank very much Jérôme Pasquet and Lionel Pibre for scientific discussions.

Author information

Authors and Affiliations

  1. LIRMM, University of Montpellier/CNRS, Montpellier, France

    Sébastien Villon & Marc Chaumont

  2. University of Nîmes, Nîmes, France

    Marc Chaumont & Gérard Subsol

  3. MARBEC, IRD/Ifremer/University of Montpellier/CNRS, Montpellier, France

    Sébastien Villéger, Thomas Claverie & David Mouillot

Authors
  1. Sébastien Villon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Chaumont
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gérard Subsol
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sébastien Villéger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Claverie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Mouillot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sébastien Villon .

Editor information

Editors and Affiliations

  1. Université Paris-Sud 11 , Orsay, France

    Jacques Blanc-Talon

  2. University of Salento , Lecce, Lecce, Italy

    Cosimo Distante

  3. Ghent University , Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre , Sydney, New South Wales, Australia

    Dan Popescu

  5. University of Antwerp , Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Villon, S., Chaumont, M., Subsol, G., Villéger, S., Claverie, T., Mouillot, D. (2016). Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48680-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48679-6

  • Online ISBN: 978-3-319-48680-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation