LifeCLEF 2016: Multimedia life species identification challenges

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9822 LNCS, Issue , 2016, Pages 286-310

  Joly, Alexis ^a   Goëau, Hervè ^b   Glotin, Hervè ^c   Spampinato, Concetto ^d   Bonnet, Pierre ^b   Vellinga, Willem Pier ^e   Champ, Julien ^a   Planquè, Robert ^e   Palazzo, Simone ^d   Müller, Henning ^f  

^a LIRMM   (France)

^b UMR AMAP   (France)

^c UNIVERSITÉ DE TOULON   (France)

^d UNIVERSITY OF CATANIA   (Italy)

^e Xeno canto Foundation   (Netherlands)

^f UNIVERSITY OF APPLIED SCIENCES WESTERN SWITZERLAND   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION REACTIONS; CLASSIFICATION (OF INFORMATION); GEOGRAPHICAL DISTRIBUTION;

ENVIRONMENTAL STAKEHOLDERS; IDENTIFICATION TOOLS; MONITORING EQUIPMENT; MULTIMEDIA INFORMATION RETRIEVAL; SPECIES IDENTIFICATION; STATE OF THE ART; STRUCTURED COMMUNITIES; USAGE SCENARIOS;

MULTIMEDIA SYSTEMS;

EID: 84984824058     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-44564-9_26     Document Type: Conference Paper

References (72)

1. MAED 2012: Proceedings of the 1st ACM International Workshop on Multimedia Analysis for Ecological Data. NY, USA, 433127. ACM, New York (2012)
2. Aptoula, E., Yanikoglu, B.: Morphological features for leaf based plant recognition. In: Proceedings IEEE International Conference Image Process. Melbourne, Australia (2013)
3. Baillie, J.E.M., H.T.C., Stuart, S.: 2004 iucn red list of threatened species. a global species assessment. IUCN, Gland, Switzerland and Cambridge, UK (2004)
4. Tòth, B.P., Màrton Tòth, D.P., Szùcs, G.: Deep learning and svm classification for plant recognition in content-based large scale image retrieval. In: Working notes of CLEF 2016 Conference (2016)
5. Bendale, A., Boult, T.E.: Towards open world recognition. CoRR (2014)
6. Boom, B.J., He, J., Palazzo, S., Huang, P.X., Beyan, C., Chou, H.M., Lin, F.P., Spampinato, C., Fisher, R.B.: A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Ecol. Inf. 23, 83–97 (2014)
7. Briggs, F., Huang, Y., Raich, R., Eftaxias, K.Z.L., et al.: The 9th mlsp competition: New methods for acoustic classification of multiple simultaneous bird species in noisy environment. In: IEEE Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–8 (2013)
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. J. Acoust. Soc. Am. 131, 4640 (2012)
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 (2007)
10. Cerutti, G., Tougne, L., Vacavant, A., Coquin, D.: A parametric active polygon for leaf segmentation and shape estimation. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 202–213. Springer, Heidelberg (2011)
11. Choi, S.: Fish identification in underwater video with deep convolutional neural network: Snumedinfo at lifeclef fish task 2015. In: Working Notes of CLEF (2015)
12. Concetto, S., Palazzo, S., Fisher, B., Boom, B.: Lifeclef fish identification task 2014. In: CLEF working Notes 2014 (2014)
13. Concetto, S., Palazzo, S., Fisher, B., Boom, B.: Lifeclef fish identification task 2015. In: CLEF Working Notes 2015 (2015)
14. Dävid Papp, D.L., Szücs, G.: Object detection, classification, tracking and individual recognition for sea images and videos. In: Working Notes of CLEF (2016)
15. Dufour, O., Artieres, T., Glotin, H., Giraudet, P.: Clusterized mfcc and svm for bird song. In: Identification, Soundscape Semiotics, Localization, Categorization (2014)
16. Dufour, O., Glotin, H., Artieres, T., Bas, Y., Giraudet, P.: Multi-instance multilabel acoustic classification of plurality of animals: birds, insects & amphibian. In: 1st Workshop on Neural information Proceedings Scaled for Bioacoustics, pp. 164–174. in conj. with NIPS (2013)
17. Dugan, P., Zollweg, J., Popescu, M., Risch, D., Glotin, H., LeCun, Y., Clark, C.: High performance computer acoustic data accelerator: A new system for exploring marine mammal acoustics for big data applications (2015)
18. Sprengel, E., Martin Jaggi, Y.K., Hofmann, T.: Audio based bird species identification using deep learning techniques. In: Working Notes of CLEF (2016)
19. Evans, F.: Detecting fish in underwater video using the em algorithm. In: Proceedings of the 2003 International Conference on Image Processing, 2003. ICIP 2003. vol. 3, pp. III-1029-32 vol. 2 (2003)
20. 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)
21. Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? 359(1444), 655–667 (2004)
22. Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Open-set plant identification using an ensemble of deep convolutional neural networks. In: Working notes of CLEF (2016)
23. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2013). CoRR abs/1311.2524
24. Glotin, H., Clark, C., LeCun, Y., Dugan, P., Halkias, X., Sueur, J.: Bioacoustic challenges in icml4b. In: Proceedings of 1st workshop on Machine Learning for Bioacoustics. No. USA, ISSN 979–10-90821-02-6 (2013). http://sabiod.org/ICML4B2013 proceedings.pdf
25. Glotin, H., Dufour, O., Bas, Y.: Overview of the 2nd challenge on acoustic bird classification. In: Glotin, H., LeCun, Y., Artières, T., Mallat, S., Tchernichovski, O., Halkias, X., (eds.) Proceedings of the Neural Information Processing Scaled for Bioacoustics. NIPS International Conference, USA (2013). http://sabiod.univ-tln.fr/nips4b
26. Goëau, H., Bonnet, P., Joly, A.: Plant identification in an open-world (lifeclef 2016). In: CLEF Working Notes 2016 (2016)
27. Goëau, H., Bonnet, P., Joly, A., Bakic, V., Barthèlèmy, D., Boujemaa, N., Molino, J.F.: The Imageclef 2013 plant identification task. In: CLEF Valencia (2013)
28. Goëau, H., Bonnet, P., Joly, A., Boujemaa, N., Barthèlèmy, D., Molino, J.F., Birnbaum, P., Mouysset, E., Picard, M.: The imageclef 2011 plant images classification task. In: CLEF 2011 (2011)
29. Goëau, H., Bonnet, P., Joly, A., Yahiaoui, I., Barthèlèmy, D., Boujemaa, N., Molino, J.F.: Imageclef 2012 plant images identification task. In: CLEF 12. Rome
30. Goëau, H., Champ, J., Joly, A.: Floristic participation at lifeclef 2016 plant identification task. In: Working notes of CLEF 2016 Conference (2016)
31. Goëau, H., Glotin, H., Planquè, R., Vellinga, W.P., Joly, A.: Lifeclef bird identification task 2016. In: CLEF working notes 2016 (2016)
32. Goëau, H., Glotin, H., Vellinga, W.P., Planque, R., Rauber, A., Joly, A.: Lifeclef bird identification task 2015. In: CLEF working notes 2015 (2015)
33. 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 (2011)
34. Hang, S.T., Tatsuma, A., Aono, M.: Bluefield (kde tut) at lifeclef 2016 plant identification task. In: Working Notes of CLEF 2016 Conference (2016)
35. Hazra, A., Deb, K., Kundu, S., Hazra, P., et al.: Shape oriented feature selection for tomato plant identification. Int. J. Comput. Appl. Technol. Res. 2(4), 449–454 (2013). 449–meta
36. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint (2015). arXiv:1512.03385
37. Jäger, J., Rodner, E., Denzler, J., Wolff, V., Fricke-Neuderth, K.: Seaclef 2016: Object proposal classification for fish detection in underwater videos. In: Working Notes of CLEF 2016 Conference (2016)
38. Joly, A., Goëau, H., Bonnet, P., Bakić, V., Barbe, J., Selmi, S., Yahiaoui, I., Carrè, J., Mouysset, E., Molino, J.F., et al.: Interactive plant identification based on social image data. Ecol. Inf. 23, 22–34 (2014)
39. Joly, A., Goëau, H., Bonnet, P., Bakic, V., Molino, J.F., Barthèlèmy, D., Boujemaa, N.: The imageclef plant identification task 2013. In: International Workshop on Multimedia Analysis for Ecological Data (2013)
40. Joly, A., Lombardo, J.C., Champ, J., Saloma, A.: Unsupervised individual whales identification: spot the difference in the ocean. In: Working Notes of CLEF (2016)
41. Kebapci, H., Yanikoglu, B., Unal, G.: Plant image retrieval using color, shape and texture features. Comput. J. 54(9), 1475–1490 (2011)
42. 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)
43. 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)
44. Lasseck, M.: Improved automatic bird identification through decision tree based feature selection and bagging. In: Working Notes of CLEF 2015 Conference (2015)
45. Lasseck, M.: Improving bird identification using multiresolution template matching and feature selection during training. In: Working Notes of CLEF Conference (2016)
46. 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)
47. Lee, S.H., Chang, Y.L., Chan, C.S., Remagnino, P.: Plant identification system based on a convolutional neural network for the lifeclef 2016 plant classification task. In: Working Notes of CLEF 2016 Conference (2016)
48. McCool, C., Ge, Z., Corke, P.: Feature learning via mixtures of dcnns for finegrained plant classification. In: Working Notes of CLEF 2016 Conference (2016)
49. 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, 2005. SIBGRAPI 2005, pp. 367–374 (2005)
50. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, December 2008
51. Piczak, K.: Recognizing bird species in audio recordings using deep convolutional neural networks. In: Working Notes of CLEF 2016 Conference (2016)
52. Pimentel, D., Zuniga, R., Morrison, D.: Update on the environmental and economic costs associated with alien-invasive species in the united states. Ecol. Econ. 52(3), 273–288 (2005)
53. 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. Photogram. Rec. 30(149), 46–62 (2015)
54. Ricard, J., Glotin, H.: Bag of mfcc-based words for bird identification. In: Working notes of CLEF 2016 Conference (2016)
55. 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., Àlvarez-Sànchez, J.R., Lòpez, F.P., Toledo-Moreo, F.J., Adeli, H. (eds.) Bioinspired Computation in Artificial Systems. LNCS, vol. 9108, pp. 419–428. Springer, Heidelberg (2015)
56. Scheirer, W.J., Jain, L.P., Boult, T.E.: Probability models for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36, 2317–2324 (2014)
57. 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 Technol. Soc. J. 49(2), 99–106 (2015)
58. Silvertown, J., Harvey, M., Greenwood, R., Dodd, M., Rosewell, J., Rebelo, T., Ansine, J., McConway, K.: Crowdsourcing the identification of organisms: A casestudy of ispot. ZooKeys 480, 125 (2015)
59. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
60. Spampinato, C., Palazzo, S., Joalland, P., Paris, S., Glotin, H., Blanc, K., Lingrand, D., Precioso, F.: Fine-grained object recognition in underwater visual data. Multimedia Tools and Applcations (MTAP-D-14-00618) (2014)
61. Spampinato, C., Beauxis-Aussalet, E., Palazzo, S., Beyan, C., van Ossenbruggen, J., He, J., Boom, B., Huang, X.: A rule-based event detection system for real-life underwater domain. Mach. Vis. Appl. 25(1), 99–117 (2014)
62. Spampinato, C., Chen-Burger, Y.H., Nadarajan, G., Fisher, R.B.: Detecting, tracking and counting fish in low quality unconstrained underwater videos. VISAPP 2, 514–519 (2008). Citeseer
63. Spampinato, C., Palazzo, S., Boom, B., van Ossenbruggen, J., Kavasidis, I., Di Salvo, R., Lin, F.P., Giordano, D., Hardman, L., Fisher, R.B.: Understanding fish behavior during typhoon events in real-life underwater environments. Multimedia Tools Appl. 70(1), 199–236 (2014)
64. Śulc, M., Mishkin, D., Matas, J.: Very deep residual networks with maxout for plant identification in the wild. In: Working Notes of CLEF 2016 Conference (2016)
65. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR (2014)
66. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
67. Tòth, B.P., Czeba, B.: Convolutional neural networks for large-scale bird song classification in noisy environment. In: Working Notes of CLEF Conference (2016)
68. Towsey, M., Planitz, B., Nantes, A., Wimmer, J., Roe, P.: A toolbox for animal call recognition. Bioacoustics 21(2), 107–125 (2012)
69. 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. J. Acoust. Soc. Am. 123, 2424 (2008)
70. Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013)
71. Weber, E., Gut, D.: Assessing the risk of potentially invasive plant species in central europe. J. Nat. Conserv. 12(3), 171–179 (2004)
72. Weber, E., Sun, S.G., Li, B.: Invasive alien plants in china: diversity and ecological insights. Biol. Invasions 10(8), 1411–1429 (2008)