Skip to main content
SpringerLink
Log in

LifeCLEF 2015: Multimedia Life Species Identification Challenges

  • Conference paper
  • First Online:
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9283))

Abstract

Using multimedia identification tools is considered as one of the most promising solutions to help bridging the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g. eBird, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipments have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching the real world’s requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 living worlds. Each task is based on large and real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders in order to reflect realistic usage scenarios. This paper presents more particularly the 2015 edition of LifeCLEF. For each of the three tasks, we report the methodology and the data sets as well as the raw results and the main outcomes.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. MAED 2012: Proceedings of the 1st ACM International Workshop on Multimedia Analysis for Ecological Data. ACM, New York (2012). 433127

    Google Scholar 

  2. Proc. of the first workshop on Machine Learning for Bioacoustics (2013)

    Google Scholar 

  3. Aptoula, E., Yanikoglu, B.: Morphological features for leaf based plant recognition. In: Proc. IEEE Int. Conf. Image Process., Melbourne, Australia, p. 7 (2013)

    Google Scholar 

  4. Backes, A.R., Casanova, D., Bruno, O.M.: Plant leaf identification based on volumetric fractal dimension. International Journal of Pattern Recognition and Artificial Intelligence 23(6), 1145–1160 (2009)

    Article  Google Scholar 

  5. Hilton-Taylor, C., Baillie, J.E.M., Stuart, S.: 2004 iucn red list of threatened species: a global species assessment. IUCN, Gland, Switzerland and Cambridge, UK (2004)

    Google Scholar 

  6. Bishop, C.M., et al.: Pattern recognition and machine learning, vol. 4. Springer New York (2006)

    Google Scholar 

  7. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  Google Scholar 

  8. Briggs, F., Lakshminarayanan, B., Neal, L., Fern, X.Z., Raich, R., Hadley, S.J., Hadley, A.S., Betts, M.G.: Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach. The Journal of the Acoustical Society of America 131, 4640 (2012)

    Article  Google Scholar 

  9. Cai, J., Ee, D., Pham, B., Roe, P., Zhang, J.: Sensor network for the monitoring of ecosystem: bird species recognition. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, pp. 293–298, December 2007

    Google Scholar 

  10. Cerutti, G., Tougne, L., Vacavant, A., Coquin, D.: A parametric active polygon for leaf segmentation and shape estimation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang, S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., Di Verdi, S., Yi-Jen, C., Ming, J. (eds.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 202–213. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Champ, J., Lorieul, T., Servajean, M., Joly, A.: A comparative study of fine-grained classification methods in the context of the lifeclef plant identification challenge 2015. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  12. Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: Pcanet: a simple deep learning baseline for image classification? arXiv preprint arXiv:1404.3606 (2014)

  13. Choi, S.: Plant identification with deep convolutional neural network: Snumedinfo at lifeclef plant identification task 2015. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  14. Concetto, S., Palazzo, S., Fisher, B., Boom, B.: Lifeclef fish identification task 2014. In: CLEF Working Notes 2015 (2015)

    Google Scholar 

  15. Dufour, O., Artieres, T., Glotin, H., Giraudet, P.: Clusterized mel filter cepstral coefficients and support vector machines for bird song idenfication (2013)

    Google Scholar 

  16. Evans, F.: Detecting fish in underwater video using the em algorithm. In: Proceedings of the 2003 International Conference on Image Processing, ICIP 2003, vol. 3, pp. III-1029-32 vol. 2, September 2003

    Google Scholar 

  17. Farnsworth, E.J., Chu, M., Kress, W.J., Neill, A.K., Best, J.H., Pickering, J., Stevenson, R.D., Courtney, G.W., VanDyk, J.K., Ellison, A.M.: Next-generation field guides. BioScience 63(11), 891–899 (2013)

    Article  Google Scholar 

  18. K. J. Gaston and M. A. O’Neill. Automated species identification: why not? 359(1444), 655–667 (2004)

    Google Scholar 

  19. Ge, Z., Mccool, C., Corke, P.: Content specific feature learning for fine-grained plant classification. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  20. Glotin, H., Sueur, J.: Overview of the first international challenge on bird classification (2013)

    Google Scholar 

  21. Goëau, H., Bonnet, P., Joly, A., Bakić, V., Barbe, J., Yahiaoui, I., Selmi, S., Carré, J., Barthélémy, D., Boujemaa, N., et al.: Pl@ ntnet mobile app. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 423–424. ACM (2013)

    Google Scholar 

  22. Goëau, H., Bonnet, P., Joly, A., Boujemaa, N., Barthélémy, D., Molino, J.-F., Birnbaum, P., Mouysset, E., Picard, M.: The ImageCLEF 2011 plant images classification task. In: CLEF Working Notes (2011)

    Google Scholar 

  23. Goëau, H., Bonnet, P., Joly, A., Yahiaoui, I., Barthelemy, D., Boujemaa, N., Molino, J.-F.: The imageclef 2012 plant identification task. In: CLEF Working Notes (2012)

    Google Scholar 

  24. Goëau, H., Glotin, H., Vellinga, W.-P., Planque, R., Rauber, A., Joly, A.: Lifeclef bird identification task 2015. In: CLEF Working Notes 2015 (2015)

    Google Scholar 

  25. Goëau, H., Glotin, H., Vellinga, W.-P., Rauber, A.: Lifeclef bird identification task 2014. In: CLEF Working Notes 2014 (2014)

    Google Scholar 

  26. Goëau, H., Joly, A., Bonnet, P.: Lifeclef plant identification task 2015. In: CLEF Working Notes 2015 (2015)

    Google Scholar 

  27. Goëau, H., Joly, A., Selmi, S., Bonnet, P., Mouysset, E., Joyeux, L., Molino, J.-F., Birnbaum, P., Bathelemy, D., Boujemaa, N.: Visual-based plant species identification from crowdsourced data. In: ACM Conference on Multimedia, pp. 813–814 (2011)

    Google Scholar 

  28. Hazra, A., Deb, K., Kundu, S., Hazra, P., et al.: Shape oriented feature selection for tomato plant identification. International Journal of Computer Applications Technology and Research 2(4), 449–454 (2013)

    Article  Google Scholar 

  29. Joly, A., Champ, J., Buisson, O.: Shared nearest neighbors match kernel for bird songs identification - lifeclef 2015 challenge. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  30. Joly, A., Goëau, H., Bonnet, P., Bakić, V., Barbe, J., Selmi, S., Yahiaoui, I., Carré, J., Mouysset, E., Molino, J.-F., et al.: Interactive plant identification based on social image data. Ecological Informatics 23, 22–34 (2014)

    Article  Google Scholar 

  31. Joly, A., Goëau, H., Bonnet, P., Bakic, V., Molino, J.-F., Barthélémy, D., Boujemaa, N.: The imageclef plant identification task 2013. In: International Workshop on Multimedia Analysis for Ecological Data, Barcelone, Espagne, October 2013

    Google Scholar 

  32. Joly, A., Müller, H., Goëau, H., Glotin, H., Spampinato, C., Rauber, A., Bonnet, P., Vellinga, W.-P., Fisher, B.: Lifeclef 2014: multimedia life species identification challenges

    Google Scholar 

  33. Kebapci, H., Yanikoglu, B., Unal, G.: Plant image retrieval using color, shape and texture features. The Computer Journal 54(9), 1475–1490 (2011)

    Article  Google Scholar 

  34. Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B.: Leafsnap: a computer vision system for automatic plant species identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 502–516. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  35. Lasseck, M.: Improved automatic bird identification through decision tree based feature selection and bagging. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  36. Le, T.-L., Dng, D.N., Vu, H., Nguyen, T.-N.: Mica at lifeclef 2015: multi-organ plant identification. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  37. Lee, D.-J., Schoenberger, R.B., Shiozawa, D., Xu, X., Zhan, P.: Contour matching for a fish recognition and migration-monitoring system. In: Optics East, pp. 37–48. International Society for Optics and Photonics (2004)

    Google Scholar 

  38. Meza, I., Espino-Gamez, A., Solano, F., Villarreal, E\(.\)

    Google Scholar 

  39. Mokhov, S.A.: A marfclef approach to lifeclef 2015 tasks. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  40. Morais, E., Campos, M., Padua, F., Carceroni, R.: Particle filter-based predictive tracking for robust fish counting. In: 18th Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2005. pp. 367–374, October 2005

    Google Scholar 

  41. E. A. O. M. Mostafa Mehdipour Ghazi, Yanikoglu, B., Ozdemir, M.C.: Sabanci-okan system in lifeclef 2015 plant identification competition. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  42. Mouine, S., Yahiaoui, I., Verroust-Blondet, A.: Advanced shape context for plant species identification using leaf image retrieval. In: ACM International Conference on Multimedia Retrieval, pp. 49:1–49:8 (2012)

    Google Scholar 

  43. Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds.): ImageCLEF – Experimental Evaluation in Visual Information Retrieval. The Springer International Series on Information Retrieval, vol. 32. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  44. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  45. Ravanbakhsh, M., Shortis, M.R., Shafait, F., Mian, A., Harvey, E.S., Seager, J.W.: Automated fish detection in underwater images using shape-based level sets. The Photogrammetric Record 30(149), 46–62 (2015)

    Article  Google Scholar 

  46. Reyes, A.K., Caicedo, J.C., Camargo, J.E.: Fine-tuning deep convolutional networks for plant recognition. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  47. Rodriguez, A., Rico-Diaz, A.J., Rabuñal, J.R., Puertas, J., Pena, L.: Fish monitoring and sizing using computer vision. In: Vicente, J.M.F., Álvarez-Sánchez, J.R., López, F.P., Toledo-Moreo, F.J., Adeli, H. (eds.) Bioinspired Computation in Artificial Systems. LNCS, vol. 9108, pp. 419–428. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  48. Shortis, M.R., Ravanbakskh, M., Shaifat, F., Harvey, E.S., Mian, A., Seager, J.W., Culverhouse, P.F., Cline, D.E., Edgington, D.R.: A review of techniques for the identification and measurement of fish in underwater stereo-video image sequences. In: SPIE Optical Metrology 2013, pp. 87910G–87910G. International Society for Optics and Photonics (2013)

    Google Scholar 

  49. Sigler, M., DeMaster, D., Boveng, P., Cameron, M., Moreland, E., Williams, K., Towler, R.: Advances in methods for marine mammal and fish stock assessments: Thermal imagery and camtrawl. Marine Technology Society Journal 49(2), 99–106, 2015–03-01T00:00:00

    Google Scholar 

  50. Spampinato, C., Chen-Burger, Y.-H., Nadarajan, G., Fisher, R.B.: Detecting, tracking and counting fish in low quality unconstrained underwater videos. In: VISAPP (2), pp. 514–519. Citeseer (2008)

    Google Scholar 

  51. Stowell, D.: Birdclef 2015 submission: unsupervised feature learning from audio. In: Working Notes of CLEF 2015 Conference (2015)

    Google Scholar 

  52. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014)

  53. Towsey, M., Planitz, B., Nantes, A., Wimmer, J., Roe, P.: A toolbox for animal call recognition. Bioacoustics 21(2), 107–125 (2012)

    Article  Google Scholar 

  54. Trifa, V.M., Kirschel, A.N., Taylor, C.E., Vallejo, E.E.: Automated species recognition of antbirds in a mexican rainforest using hidden markov models. The Journal of the Acoustical Society of America 123, 2424 (2008)

    Article  Google Scholar 

  55. Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. International Journal of Computer Vision 104(2), 154–171 (2013)

    Article  Google Scholar 

  56. Voorhees, E.M., et al.: The trec-8 question answering track report. In: TREC, vol. 99, pp. 77–82 (1999)

    Google Scholar 

  57. Wheeler, Q.D., Raven, P.H., Wilson, E.O.: Taxonomy: Impediment or expedient? Science 303(5656), 285 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inria, LIRMM, Montpellier, France

    Alexis Joly

  2. Inria, Montpellier, France

    Hervé Goëau

  3. IUF & University de Toulon, Toulon, France

    Hervé Glotin

  4. University of Catania, Catania, Italy

    Concetto Spampinato & Simone Palazzo

  5. CIRAD-Amap, Montpellier, France

    Pierre Bonnet

  6. Xeno-canto Foundation, Drachten, The Netherlands

    Willem-Pier Vellinga & Robert Planqué

  7. Vienna University of Technology, Vienna, Austria

    Andreas Rauber

  8. University of Edinburgh, Edinburgh, UK

    Bob Fisher

  9. HES-SO, Sierre, Switzerland

    Henning Müller

Authors
  1. Alexis Joly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hervé Goëau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hervé Glotin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Concetto Spampinato
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pierre Bonnet
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Willem-Pier Vellinga
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert Planqué
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andreas Rauber
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Simone Palazzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bob Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Henning Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexis Joly .

Editor information

Editors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Toulouse , France

    Josanne Mothe

  2. Department of Computer Science, University of Neuchatel, Neuchâtel, Switzerland

    Jacques Savoy

  3. Faculteit der Geesteswetenschappen, Universiteit Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  4. Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Karen Pinel-Sauvagnat

  5. School of Computing, Dublin City University, Dublin, Ireland

    Gareth Jones

  6. LIA - CERI, Université d'Avignon et des Pays de Vaucluse, Avignon, France

    Eric San Juan

  7. Department of Information Engineering, University of Padua, Padua, Italy

    Linda Capellato

  8. of Information Engineering (DEI), University of Padua, Department, Padova, Italy

    Nicola Ferro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Joly, A. et al. (2015). LifeCLEF 2015: Multimedia Life Species Identification Challenges. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24027-5_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation