Segmentation of Saimaa Ringed Seals for Identification Purposes

  • Artem Zhelezniakov
  • Tuomas EerolaEmail author
  • Meeri Koivuniemi
  • Miina Auttila
  • Riikka Levänen
  • Marja Niemi
  • Mervi Kunnasranta
  • Heikki Kälviäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


Wildlife photo-identification is a commonly used technique to identify and track individuals of wild animal populations over time. It has various applications in behavior and population demography studies. Nowadays, mostly due to large and labor-intensive image data sets, automated photo-identification is an emerging research topic. In this paper, the first steps towards automatic individual identification of the critically endangered Saimaa ringed seal (Phoca hispida saimensis) are taken. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual making the image-based identification possible. We propose a superpixel classification based method for the segmentation of ringed seal in images to eliminate the background and to simplify the identification. The proposed segmentation method is shown to achieve a high segmentation accuracy with challenging image data. Furthermore, we show that using the obtained segmented images promising identification results can be obtained even with a simple texture feature based approach. The proposed method uses general texture classification techniques and can be applied also to other animal species with a unique fur or skin pattern.


Texture Feature Support Vector Machine Classifier Scale Invariant Feature Transform Ringed Seal Camera Trap 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the Wildlife Photo-ID Network funded by the Finnish Cultural Foundation.


  1. 1.
    Kovacs, K.M., Aguilar, A., Aurioles, D., Burkanov, V., Campagna, C., Gales, N., Gelatt, T., Goldsworthy, S.D., Goodman, S.J., Hofmeyr, G.J.G., Härkönen, T., Lowry, L., Lydersen, C., Schipper, J., Sipilä, T., Southwell, C., Stuart, S., Thompson, D., Trillmich, F.: Global threats to pinnipeds. Mar. Mammal Sci. 28, 414–436 (2012)CrossRefGoogle Scholar
  2. 2.
    Auttila, M., Niemi, M., Skrzypczak, T., Viljanen, M., Kunnasranta, M.: Estimating and mitigating perinatal mortality in the endangered saimaa ringed seal (phoca hispida saimensis) in a changing climate. Annal. Zool. Fenn. 51, 526–534 (2014)CrossRefGoogle Scholar
  3. 3.
    Koivuniemi, M., Auttila, M., Niemi, M., Levänen, R., Kunnasranta, M.: Photo-ID as a tool for studying and monitoring the critically endangered saimaa ringed seal. (2015) manuscript under reviewGoogle Scholar
  4. 4.
    Anderson, C.J.: Individual identification of polar bears by whisker spot patterns. Ph.D. thesis, University of Central Florida, Orlando, Florida (2007)Google Scholar
  5. 5.
    Tharwat, A., Gaber, T., Hassanien, A., Hassanien, H.A., Tolba, M.F.: Cattle identification using muzzle print images based on texture features approach. In: Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 217–227 (2014)Google Scholar
  6. 6.
    Hoque, S., Azhar, M., Deravi, F.: ZOOMETRICS-biometric identification of wildlife using natural body marks. Int. J. Bio-Sci. Bio-Technol. 3, 45–53 (2011)Google Scholar
  7. 7.
    Halloran, K.M., Murdoch, J.D., Becker, M.S.: Applying computer-aided photo-identification to messy datasets: a case study of Thornicroft’s giraffe (Giraffa camelopardalis thornicrofti). Afr. J. Ecol. 53, 147–155 (2014)CrossRefGoogle Scholar
  8. 8.
    Bendik, N.F., Morrison, T.A., Gluesenkamp, A.G., Sanders, M.S., O’Donnell, L.J.: Computer-assisted photo identification outperforms visible implant elastomers in an endangered salamander, Eurycea tonkawae. PLoS One 8, e59424 (2013)CrossRefGoogle Scholar
  9. 9.
    Albu, A.B., Wiebe, G., Govindarajulu, P., Engelstoft, C., Ovatska, K.: Towards automatic modelbased identification of individual sharp-tailed snakes from natural body markings. In: Proceedings of ICPR Workshop on Animal and Insect Behaviour, Tampa, FL, USA (2008)Google Scholar
  10. 10.
    Yılmaz Kaya, L.K., Tekin, R.: A computer vision system for the automatic identification of butterfly species via gabor-filter-based texture features and extreme learning machine: GF+ ELM. TEM J. 2, 13–20 (2013)Google Scholar
  11. 11.
    Adams, J.D., Speakman, T., Zolman, E., Schwacke, L.H.: Automating image matching, cataloging, and analysis for photo-identification research. Aquat. Mammals 32, 374 (2006)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Crall, J., Stewart, C., Berger-Wolf, T., Rubenstein, D., Sundaresan, S.: Hotspotter - patterned species instance recognition. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 230–237 (2013)Google Scholar
  14. 14.
    Yu, X., Wang, J., Kays, R., Jansen, P., Wang, T., Huang, T.: Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 2013, 52 (2013)zbMATHCrossRefGoogle Scholar
  15. 15.
    Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.W.K., Tan, N.M., Tao, D., Cheng, C.Y., Aung, T., Wong, T.Y.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 32, 1019–1032 (2013)CrossRefGoogle Scholar
  16. 16.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)CrossRefGoogle Scholar
  17. 17.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: An empirical evaluation. IEEE Conf. Comput. Vis. Pattern Recogn. 2009, 2294–2301 (2009)Google Scholar
  18. 18.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision vol. 2, pp. 416–423 (2001)Google Scholar
  19. 19.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  20. 20.
    Costa, A., Humpire-Mamani, G., Traina, A.: An efficient algorithm for fractal analysis of textures. In: 25th Conference on Graphics, Patterns and Images vol. 2012, pp. 39–46 (2012)Google Scholar
  21. 21.
    Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  22. 22.
    Phillips, P.J., Moon, H., Rauss, P.J., Rizvi, S.: The feret evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Artem Zhelezniakov
    • 1
    • 3
  • Tuomas Eerola
    • 1
    Email author
  • Meeri Koivuniemi
    • 2
  • Miina Auttila
    • 2
    • 4
  • Riikka Levänen
    • 2
  • Marja Niemi
    • 2
  • Mervi Kunnasranta
    • 2
  • Heikki Kälviäinen
    • 1
    • 5
  1. 1.Machine Vision and Pattern Recognition Laboratory, School of Engineering ScienceLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.Department of BiologyUniversity of Eastern FinlandJoensuuFinland
  3. 3.Department of Computer Technologies and Control SystemsITMO UniversitySaint PetersburgRussia
  4. 4.Parks and Wildlife FinlandMetsähallitusSavonlinnaFinland
  5. 5.School of Information TechnologyMonash University MalaysiaBandar SunwayMalaysia

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