Abstract
The analysis of natural images has been the topic of research in uncountable articles in computer vision and pattern recognition (e.g., natural images has been used as benchmarks for object recognition and image retrieval). However, despite the research progress in such field, there is a gap in the analysis of certain type of natural images, for instance, those in the context of animal behavior. In fact, biologists perform the analysis of natural images manually without the aid of techniques that were supposedly developed for this purpose. In this context, this paper presents a study on automated methods for the analysis of natural images of hummingbirds with the goal to assist biologists in the study of animal behavior. The automated analysis of hummingbird behavior is challenging mainly because of (1) the speed at which these birds move and interact; (2) the unpredictability of their trajectories; and (3) its camouflage skills. We report a comparative study of two deep learning approaches for the detection of hummingbirds in their nest. Two variants of transfer learning from convolutional neural networks (CNNs) are evaluated in real imagery for hummingbird behavior analysis. Transfer learning is adopted because not enough images are available for training a CNN from scratch, besides, transfer learning is less time consuming. Experimental results are encouraging, as acceptable classification performance is achieved with CNN-based features. Interestingly, a pretrained CNN without fine tunning and a standard classifier performed better in the considered data set.
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References
Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.A.: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47979-1_7
Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. J. Mach. Learn. Res. 3(Feb), 1107–1135 (2003)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceedings of CVPRW, p. 178 (2004)
Griffin, G., Holub, G., Perona, P.: The caltech-256. Technical report. California Institute of Technology, Pasadena, California (2007)
Everingham, M., Zisserman, A., Williams, C.K.I., Van Gool, L.: The PASCAL Visual Object Classes Challenge 2006 (VOC2006) Results. http://www.pascal-network.org/challenges/VOC/voc2006/results.pdf
Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)
Fink, M., Ullman, S.: From aardvark to zorro: a benchmark for mammal image classification. Int. J. Comput. Vis. 77(1), 143–156 (2008)
Escalante, H.J., Hernández, C.A., Gonzalez, J.A., López-López, A., Montes, M., Morales, E.F., Sucar, L.E., Villaseñor, L., Grubinger, M.: The segmented and annotated IAPR TC-12 benchmark. Comput. Vis. Image Underst. 114(4), 419–428 (2010)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
del Coro Arizmendi, M., Rodríguez-Flores, C.I.: How many plant species do hummingbirds visit? Ornitol. Neotrop. 23, 71–75 (2012)
Elliott, A., del Hoyo, J., Sargatal, J.: Handbook of the Birds of the World, Volume 5, Barn-Owls to Hummingbirds, pp. 388–435. Lynx Edicions, Barcelona (1999)
Colwell, R.K.: Rensch’s rule crosses the line: convergent allometry of sexual size dimorphism in hummingbirds and flower mites. Am. Nat. 156(5), 495–510 (2000)
Bleiweiss, R.: Phylogeny, body mass, and genetic cponsequences of lek-mating behavior in hummingbirds (1998)
Johnsgard, P.A.: The Hummingbirds of North America. Smithsonian Institution, Washington (2016)
Vleck, C.M.: Hummingbird incubation: female attentiveness and egg temperature. Oecologia 51(2), 199–205 (1981)
Baltosser, W.H.: Nesting success and productivity of hummingbirds in Southwestern New Mexico and Southeastern Arizona. Wilson Bull. 98(3), 353–367 (1986)
Brown, B.T.: Nesting chronology, density and habitat use of black-chinned hummingbirds along the Colorado River Arizona. J. Field Ornithol. 63(4), 393–400 (1992)
Greeney, H.F., Hough, E.R., Hamilton, C.E., Wethington, S.M.: Nestling growth and plumage development of the black-chinned hummingbird (Archilochus alexandri) in Southeastern Arizona. Huitzil. Revista Mexicana de Ornitología 9(2), 35–42 (2008)
Greeney, H.F., Wethington, S.M.: Proximity to active accipiter nests reduces nest predation of black-chinned hummingbirds. Wilson J. Ornithol. 121(4), 809–812 (2009)
Smith, D.M., Finch, D.M., Hawksworth, D.L.: Black-chinned hummingbird nest-site selection and nest survival in response to fuel reduction in a Southwestern Riparian forest. Condor 111(4), 641–652 (2009)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, ICML 2010, pp. 807–814 (2010)
Memisevic, R., Zach, C., Pollefeys, M., Hinton, G.E.: Gated softmax classification. In: Advances in Neural Information Processing Systems, pp. 1603–1611 (2010)
Dai, W., Yang, Q., Xue, G.-R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. ACM (2007)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, pp. 759–766. ACM (2007)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J. Mach. Learn. Res. 10(Jul), 1633–1685 (2009)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)
Pasquale, G., Ciliberto, C., Rosasco, L., Natale, L.: Object identification from few examples by improving the invariance of a deep convolutional neural network. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 4904–4911. IEEE (2016)
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of CVPR, pp. 2818–2826 (2016)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7(Jan), 1–30 (2006)
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)
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Serrano, S.A. et al. (2018). Automated Detection of Hummingbirds in Images: A Deep Learning Approach. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_16
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