BIRD SPECIES IDENTIFICATION USING DEEP LEARNING
DOI:
https://doi.org/10.46243/jst.2022.v7.i09.pp32-40Keywords:
Tensorflow, grey scale pixels, Caltech-UCSD, AutographAbstract
Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this method uses the Caltech-UCSD Birds 200 [CUB-200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where the multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet it can predicate the required bird species by using highest score. Experimental analysis on dataset (i.e. Caltech-UCSD Birds 200 [CUB-200- 2011]) shows that algorithm achieves an accuracy of bird identification between 80% and 90%.The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library.
References
Tóth, B.P. and Czeba, B., 2016, September. Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment. In CLEF (Working Notes) (pp. 560-568)
Fagerlund, S., 2007. Bird species recognition using support vector machines. EURASIP Journal on Applied Signal Processing, 2007(1), pp.64-64
Pradelle, B., Meister, B., Baskaran, M., Springer, J. and Lethin, R., 2017, November. Polyhedral Optimization of TensorFlow Computation Graphs. In 6th Workshop on Extreme scale Programming Tools (ESPT-2017) at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC17)
Cireşan, D., Meier, U. and Schmidhuber, J., 2012. Multi-column deep neural networks for image classification. arXiv preprint arXiv:1202.2745
Andr´eia Marini, Jacques Facon and Alessandro L. Koerich Postgraduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paran´a (PUCPR) Curitiba PR, Brazil 80215–901 Bird Species Classification Based on Color Features
Image Reco gnition with Deep Learning Techniques ANDREIPETRU BĂRAR, VICTOR-EMIL NEAGOE, NICU SEBE Faculty of Electronics, Telecommunications & Information Technology Polytechnic University of Bucharest
Xception: Deep Learning with Depthwise Separable Convolutio ns François Chollet Google, Inc. Page | 40
Zagoruyko, S. and Komodakis, N., 2016. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928
Inception-v4,Inception-ResNetand the Impact of Residual Connectionson Learning Christian Szegedy, Sergey Ioffe,Vincent Vanhoucke,Alexandrr A.Alemi
Stefan Kahl, Thomas Wilhelm-Stein, Hussein Hussein, Holger Klinck, Danny Kowerko, Marc Ritter, and Maximilian Eibl Large-Scale Bird Sound Classification using Convolutional Neural Networks
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L. Alexander, David W. Jacobs, and Peter N. Belhumeur Birdsnap: Large-scaleFine-grainedVisualCategorizationofBirds
Bo Zhao, Jias hi Feng Xiao Wu Shuicheng Yan A Survey on Deep Learning-based Fine grained Object Classification and Semantic Segmentation
Yuning Chai Electrical Engineering Dept. ETH Zurich,Victor Lempitsky Dept. of Engineering Science University of Oxford, Andrew Zisserman Dept. of Engineering Science University of Oxford BiCoS:A BiSegmentation Method for Image Classification
