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 GEP  Vol.6 No.5 , May 2018
Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks
Abstract:
The only known predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subsp.) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales’ population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The “off-the-shelf” Image net-trained VGG16 CNN was used as the feature-encoder of the perpixel sematic segmentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provid-ed. Training and image augmentation procedures were developed to compen-sate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.
Cite this paper: A. Konovalov, D. , Hillcoat, S. , Williams, G. , Alastair Birtles, R. , Gardiner, N. and I. Curnock, M. (2018) Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks. Journal of Geoscience and Environment Protection, 6, 25-36. doi: 10.4236/gep.2018.65003.
References

[1]   Best, P.B. (1985) External Characters of Southern Minke Whales and the Existence of a Diminutive Form. Sci. Rep. Whales Res. Inst., 36, 1-33. http://www.icrwhale.org/pdf/SC0361-33.pdf

[2]   Curnock, M.I., Birtles, R.A. and Valentine, P.S. (2013) Increased Use Levels, Effort, and Spatial Distribution of Tourists Swimming with Dwarf Minke Whales at the Great Barrier Reef. Tourism in Marine Environments, 9, 5-17. https://doi.org/10.3727/154427313X13659574649867

[3]   Birtles, R.A., Arnold, P.W. and Dunstan, A. (2002) Commercial Swim Programs with Dwarf Minke Whales on the Northern Great Barrier Reef, Australia: Some Characteristics of the Encounters with Management Implications. Australian Mammalogy, 24, 23-38. https://doi.org/10.1071/AM02023

[4]   Gedamke, J., Costa, D.P. and Dunstan, A. (2001) Localization and Visual Verification of a Complex Minkewhale Vocalization. J. Acoust. Soc. Am., 109, 3038-3047. https://doi.org/10.1121/1.1371763

[5]   Mangott, A.H., Birtles, R.A. and Marsh, H. (2011) Attraction of Dwarf Minke Whales (Balaenoptera acutorostrata) to Vessels and Swimmers in the Great Barrier Reef World Heritage Area—The Management Challenges of an Inquisitive Whale. Journal of Ecotourism, 10, 64-76. https://doi.org/10.1080/14724041003690468

[6]   Arnold, P.W., Birtles, R.A., Dunstan, A., Lukoschek, V. and Matthews, M. (2005) Colour Patterns of the Dwarf Minke Whale Balaenoptera acutorostrata sensual to: Description, Cladistic Analysis and Taxonomic Implications. Memoirs of the Queensland Museum, 51, 277-307. https://researchonline.jcu.edu.au/4935/1/4935_Arnold_et_al...2005.pdf

[7]   Arnold, P., Marsh, H. and Heinsohn, G. (1987) The Occurrence of Two Forms of Minke Whales in East Australian Waters with Description of External Characters and Skeleton of the Diminutive Form. Sci. Rep. Whales Res. Inst., 38, 1-46. http://www.icrwhale.org/pdf/SC0381-46.pdf

[8]   Sobtzick, S. (2010) Dwarf Minke Whales in the Northern Great Barrier Reef and Implications for the Sustainable Management of the Swim-With Whales Industry. PhD Thesis, James Cook University. https://researchonline.jcu.edu.au/28199/1/28199-sobtzick-2010-thesis.pdf

[9]   Hughes, B. and Burghardt, T. (2017) Automated Visual Fin Identification of Individual Great White Sharks. International Journal of Computer Vision (IJCV), 122, 542-557. https://doi.org/10.1007/s11263-016-0961-y

[10]   Carpentier, A.S., Jean, C., Barret, M., Chassagneux, A. and Ciccione, S. (2016) Stability of Facial Scale Patterns on Green Sea Turtles Chelonia mydas over Time: A Validation for the Use of a Photo-Identification Method. Journal of Experimental Marine Biology and Ecology, 476, 15-21. https://doi.org/10.1016/j.jembe.2015.12.003

[11]   Brust, C., Burghardt, T., Groenenberg, M., Kading, C., Kuhl, H.S., Manguette, M.L. and Denzler, J. (2017) Towards Automated Visual Monitoring of Individual Gorillas in the Wild. The IEEE International Conference on Computer Vision (ICCV), 2820-2830. http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w41/Brust_Towards_Automated_Visual_ICCV_2017_paper.pdf https://doi.org/10.1109/ICCVW.2017.333

[12]   Genov, T., Centrih, T., Wright, A.J. and Wu, G.-M. (2017) Novel Method for Identifying Individual Cetaceans Using Facial Features and Symmetry: A Test Case Using Dolphins. Marine Mammal Science. (In press) https://doi.org/10.1111/mms.12451

[13]   LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deeplearning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539

[14]   Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. In: Pereira, F., Burges, C.J.C., Bottou, L. and Weinberger, K.Q., Eds., Advances in Neural Information Processing Systems, Vol. 25, Curran Associates, Inc., 1097-1105.

[15]   Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C. and Fei-Fei, L. (2015) Image Net Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211-252. https://doi.org/10.1007/s11263-015-0816-y

[16]   Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556. http://arxiv.org/abs/1409.1556

[17]   Shelhamer, E., Long, J. and Darrell, T. (2017) Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651. https://doi.org/10.1109/TPAMI.2016.2572683

[18]   Chollet, F. (2015) Keras. https://github.com/fchollet/keras

[19]   Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. and Zheng, X. (2015) Tensor Flow: Large-Scale Machine Learning on Heterogeneous Systems. http://tensorflow.org

[20]   Ju, J. (2017) Keras-FCN. https://github.com/jihongju/keras-fcn

[21]   Shelhamer, E., Long, J. and Darrell, T. (2017) FCN. https://github.com/shelhamer/fcn.berkeleyvision.org

[22]   Konovalov, D.A. (2017) Keras-FCN-8s. https://github.com/dmitryako/keras_fcn_8s

[23]   Oquab, M., Bottou, L., Laptev, I. and Sivic, J. (2014) Learning and Transferring Mid-Level Image Representations Using Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2014.222

[24]   Glorot, X. and Bengio, Y. (2010) Understanding the Difficulty of Training Deep Feedforward Neural Networks. In: Teh, Y.W. and Titterington, M., Eds., Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research (PMLR), Chia Laguna Resort, Sardinia, Italy, Vol. 9, 249-256. http://proceedings.mlr.press/v9/glorot10a.html

[25]   Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press.

[26]   Hinton, G., Srivastava, N. and Swersky, K. Overview of Mini-Batch Gradient Descent. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf

[27]   Fawcett, T. (2006) An Introduction to ROC Analysis. Pattern Recognition Letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010

 
 
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