Abstract
At present, there is no huge data set on bird classification, and the classic CUB-200-2011 data set has only 11 788 images, which are still unable to train a generalized classification and recognition model compared with ImageNet and other large data sets with millions of data. Therefore, using deep transfer learning, after tuning for bird recognition, is very valuable with large data sets training model parameters. In this paper, by comparing the training effect of common benchmark models in CUB-200-2011 dataset, ResNeXt model is selected as the transfer learning benchmark model for its well performance. Through optimizing the loss function and reducing the learning rate, the proposed model provides better performance for the data augmentation and adding the full connection layer. Compared with the benchmark model, the recognition rate of the proposed model can reach 84.43%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guangmei, Z.: A Checklist on the Classification and Distribution of the Birds of China. Beijing Science Press, Beijing (2017)
Mamalet, F., Garcia, C.: Simplifying ConvNets for Fast learning. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7553, pp. 58–65. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33266-1_8
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
Szegedy, C., Vanhoucke, V., Ioffe, S. et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning (2016). arXiv preprint arXiv:1602.07261
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
He, K,. Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Xie, S., Girshick, R., Dollár, P., et al.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Howard, A.G., Zhu, M., Chen, B., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017). arXiv preprint arXiv:1704.04861
Sandler, M., Howard, A., Zhu, M., et al.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. IEEE (2009)
Wei, X.-S., Xie, C.-W., Wu, J., et al.: Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognit. 76, 704–714 (2018)
Zheng, H., Fu, J., Mei, T., et al.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5209–5217 (2017)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-Based R-CNNs for Fine-Grained Category Detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54
Yili, Z.: Fine-grained Recognition of Yunnan Wild Bird Images Based on Deep Learning. Yunnan University, Kunming Yunnan (2018)
Xin-ye, L., Guang-bi, W.: Fine-grained bird recognition based on convolution neural network semantic detection. Sci. Technol. Eng. 18(10), 240–244 (2018)
Yuchen, W.: Fine-grained Bird Recognition Based on Deep Learning Beijing Forestry University, Beijing (2018)
Gai, K., Qiu, M., Zhao, H.: Security-aware efficient mass distributed storage approach for cloud systems in big data. In: 2016 IEEE 2nd international conference on big data security on cloud (BigDataSecurity). In: IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS). IEEE, pp. 140–145 (2016)
Qiu, M., Jia, Z., Xue, C., et al.: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. VLSI Signal Proc. Syst. Signal Image Video Technol. 46, 55–73 (2007). https://doi.org/10.1007/s11265-006-0002-0
Zhang, Q., Huang, T., Zhu, Y., et al.: A case study of sensor data collection and analysis in smart city: provenance in smart food supply chain. Int. J. Distrib. Sens. Netw. 9, (2013)
Gao, Y., Xiang, X., Xiong, N., et al.: Human action monitoring for healthcare based on deep learning. IEEE Access 6, 52277–52285 (2018)
Gao, Y., Xiong, N., Yu, W., et al.: Learning identity-aware face features across poses based on deep siamese networks. IEEE Access 7, 105789–105799 (2019)
Huang, K., Zhang, Q., Zhou, C., et al.: An efficient intrusion detection approach for visual sensor networks based on traffic pattern learning. IEEE Tran. Syst. Man Cybern. Syst. 47, 2704–2713 (2017)
Li, L-F., Wang, X., Hu, W-J., et al.: Deep Learning in Skin Disease Image Recognition: A Review. IEEE Access (2020)
Liu, M., Zhou, M., Zhang, T., et al.: Semi-supervised learning quantization algorithm with deep features for motor imagery EEG recognition in smart healthcare application. Appl. Soft Comput. 89, (2020)
Mao, Z., Su, Y., Xu, G., et al.: Spatio-temporal deep learning method for ADHD fMRI classification. Inf. Sci. 499, 1–11 (2019)
Shahzad, A., Lee, M., Lee, Y.-K., et al.: Real time MODBUS transmissions and cryptography security designs and enhancements of protocol sensitive information. Symmetry 7, 1176–1210 (2015)
Wu, C., Ju, B., Wu, Y., et al.: UAV autonomous target search based on deep reinforcement learning in complex disaster scene. IEEE Access 7, 117227–117245 (2019)
Wu, C., Luo, C., Xiong, N., et al.: A greedy deep learning method for medical disease analysis. IEEE Access 6, 20021–20030 (2018)
Wu, W., Xiong, N., Wu, C.: Improved clustering algorithm based on energy consumption in wireless sensor networks. Iet Netw. 6, 47–53 (2017)
Yang, Y., Xiong, N., Chong, N.Y., et al.: A decentralized and adaptive flocking algorithm for autonomous mobile robots. In: 2008 The 3rd International Conference on Grid and Pervasive Computing-Workshops, pp. 262–268. IEEE (2008)
Yi, B., Shen, X., Liu, H., et al.: Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans. Ind. Inf. 15, 4591–4601 (2019)
Zhang, Q., Zhou, C., Xiong, N., et al.: Multimodel-based incident prediction and risk assessment in dynamic cybersecurity protection for industrial control systems. IEEE Trans. Syst. Man Cybern. Syst. 46, 1429–1444 (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2009)
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27
Zhuang, F.Z., Luo, P., He, Q., Shi, Z.Z.: Survey on transfer learning research. Ruan Jian Xue Bao/J. Softw. 26(1), 26–39 (2015)
Wang, J.: Transfer Learning Tutorial. (2019) In p https://tutorial.transferlearning.xyz/index
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Hinton, G., Srivastava, N., Swersky, K.: Neural networks for machine learning. Coursera Video Lect. 264, (2012)
Wah, C., Branson, S., Welinder, P., et al.: The caltech-ucsd birds-200–2011 dataset (2011)
Acknowledgements
This work is supported the Fundamental Research Funds for the Central Universities (TD2014-02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, P., Wu, G., Wu, X., Yi, X., Xiong, N. (2021). Birds Classification Based on Deep Transfer Learning. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2020. Lecture Notes in Computer Science(), vol 12608. Springer, Cham. https://doi.org/10.1007/978-3-030-74717-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-74717-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-74716-9
Online ISBN: 978-3-030-74717-6
eBook Packages: Computer ScienceComputer Science (R0)