Abstract
The species information is fundamental for ensuring biodiversity. The distinguishing proof of birds by customary keys is intricate, tedious, and because of the unavailability of information about exact name, it is difficult to identify and often baffling for non-specialists. This makes a difficult to conquer leap for tenderfoots intrigued by procuring species information. Today, there is an expanding enthusiasm for computerizing the procedure of species distinguishing proof. The accessibility and pervasiveness of important advancements, such as, computerized cameras and cell phones, the remote access to databases, new strategies in picture preparing and design acknowledgment let computerized species recognizable proof move toward becoming reality. This paper presents a deep learning neural network technique for identifying bird species. Tensor flow framework is used for the implementation.
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References
Wah C, Branson S, Welinder P (2011) California Institute of Technology, The Caltech-UCSD Birds-200-2011 Dataset WahCUB_200_2011, CNS-TR-2011-001
Li J, Zhang L, Yan B (2014) Research and application of bird species identification algorithm based on image features. In: International symposium on computer, consumer and control. IEEE Computer Society. https://doi.org/10.1109/is3c.2014.47
Ertam F, Aydan G (2017) Data classification with deep learning using tensorflow. In: IEEE 2nd international conference on computer science and engineering
Chan T-H, Jia K, Gao S (2015) Effective microorganisms: deep residual learning for image recognition. In: IEEE transactions on image processing, vol 24, no 12, Dec 2015
Branson S (2012) Interactive learning and prediction algorithms for computer vision applications. UC San Diego Electronic Theses and Dissertations
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2014) ImageNet large scale visual recognition challenge. arXiv:1409.0575
Saxe AM, McClelland JL, Ganguli S (2013) Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv:1312.6120
Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) High-performance neural networks for visual object classification. Arxiv preprint. arXiv:1102.0183
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: CVPR09
Deng J, Berg A, Satheesh S, Su H, Khosla A, Fei-Fei L (2012) ILSVRC-2012
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Pillai, S.K., Raghuwanshi, M.M., Shrawankar, U. (2019). Deep Learning Neural Network for Identification of Bird Species. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_31
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DOI: https://doi.org/10.1007/978-981-13-7150-9_31
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