Image-based Animal Recognition based on Transfer Learning


Authors

DOI:

https://doi.org/10.22517/23447214.24538

Keywords:

Reconocimiento de animales, visón por computador, aprendizaje profundo, aprendizaje por transferencia.

Abstract

Automatic image-based recognition systems have been widely used to solve different computer vision tasks. In particular, animals' identification in farms is a research field of interest for the computer vision and the agriculture community. It is then necessary to develop robust and precise algorithms to support detection, recognition, and monitoring tasks to enhance farm management. Traditionally, deep learning approaches have been proposed to solve image-based detection tasks. Nonetheless, databases holding many instances are required to achieve competitive performances, not mentioning the hyperparameters tuning issues. In this paper, we propose a transfer learning approach for image-based animal recognition. We enhance a pre-trained Convolutional Neural Network model for animal classification from noisy and low-quality images. First, a dog vs. cat task is tested from the well-known CIFAR database. Further, a cow vs. no cow database is built to test our transfer learning approach. The achieved results show competitive classification performance using different types of architectures compared to state-of-the-art methodologies.

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References

[1] Qun Liu and S. Mukhopadhyay, “Unsupervised Learning using Pretrained CNN and Associative Memory Bank.” 2018. doi: 10.1109/IJCNN.2018.8489408
[2] Cai, Z., & Vasconcelos, N. (2019). Cascade R-CNN: high quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2019.2956516
[3] Liu, C., Liu, P., Zhao, W., & Tang, X. (2019). Visual Tracking by Structurally Optimizing Pre-trained CNN. IEEE Transactions on Circuits and Systems for Video Technology. doi: 10.1109/TCSVT.2019.2938038
[4] Yao, G., Lei, T., & Zhong, J. (2019). A review of Convolutional-Neural-Network-based action recognition. Pattern Recognition Letters, 118, 14-22. doi: 10.1016/j.patrec.2018.05.018
[5] Su, J., Yi, D., Su, B., Mi, Z., Liu, C., Hu, X., ... & Chen, W. H. (2020). Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring. IEEE Transactions on Industrial Informatics. doi: 10.1109/TII.2020.2979237
[6] Mukherjee, A., Misra, S., Sukrutha, A., & Raghuwanshi, N. S. (2020). Distributed aerial processing for IoT-based edge UAV swarms in smart farming. Computer Networks, 167, 107038. doi: 10.1016/j.comnet.2019.107038
[7] Bullock, J., Cuesta-Lázaro, C., & Quera-Bofarull, A. (2019, March). XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets. In Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10953, p. 109531Z). International Society for Optics and Photonics. doi: 10.1117/12.2512451
[8] Zhou, T., Ruan, S., & Canu, S. (2019). A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3, 100004. doi: 10.1016/j.array.2019.100004
[9] Xu, Q., Zhang, M., Gu, Z., & Pan, G. (2019). Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs. Neurocomputing, 328, 69-74. doi: 10.1016/j.neucom.2018.03.080
[10] Webster, R., Rabin, J., Simon, L., & Jurie, F. (2019). Detecting overfitting of deep generative networks via latent recovery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 11273-11282).
[11] Lu, N., Zhang, T., Niu, G., & Sugiyama, M. (2020, June). Mitigating overfitting in supervised classification from two unlabeled datasets: A consistent risk correction approach. In International Conference on Artificial Intelligence and Statistics (pp. 1115-1125).
[12] Li, Z., Kamnitsas, K., & Glocker, B. (2019, October). Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 402-410). Springer, Cham. doi: 10.1007/978-3-030-32248-9_98
[13] Bejani, M. M., & Ghatee, M. (2020). Theory of adaptive SVD regularization for deep neural networks. Neural Networks. doi: 10.1016/j.neunet.2020.04.021
[14] Ranjit, M. P., Ganapathy, G., Sridhar, K., & Arumugham, V. (2019, July). Efficient deep learning hyperparameter tuning using cloud infrastructure: intelligent distributed hyperparameter tuning with Bayesian optimization in the cloud. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) (pp. 520-522). IEEE. doi: 10.1109/CLOUD.2019.00097
[15] Choi, D., Shallue, C. J., Nado, Z., Lee, J., Maddison, C. J., & Dahl, G. E. (2019). On empirical comparisons of optimizers for deep learning. arXiv preprint arXiv:1910.05446.
[16] Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE. doi: 10.1109/JPROC.2020.3004555
[17] Zheng, H., Wang, R., Yang, Y., Yin, J., Li, Y., Li, Y., & Xu, M. (2019). Cross-domain fault diagnosis using knowledge transfer strategy: A review. IEEE Access, 7, 129260-129290. doi: 10.1109/ACCESS.2019.2939876
[18] Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning-based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1-6. doi: 10.1016/j.patrec.2019.03.022
[19] Sun, Q., Liu, Y., Chua, T. S., & Schiele, B. (2019). Meta-transfer learning for few-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 403-412).
[20] Sharma, N., Jain, V., & Mishra, A. (2018). An analysis of convolutional neural networks for image classification. Procedia computer science, 132, 377-384. 10.1016/j.procs.2018.05.198
[21] Liu, Q., & Mukhopadhyay, S. (2018, July). Unsupervised learning using pretrained CNN and associative memory bank. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 01-08). IEEE. 10.1109/IJCNN.2018.8489408
[22] Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.
[23] Heaton, J. (2018). Ian goodfellow, yoshua bengio, and aaron courville: Deep learning. doi: 10.1007/s10710-017-9314-z
[24] Trnovszký, T., Kamencay, P., Orješek, R., Benčo, M., & Sýkora, P. (2017). Animal recognition system based on convolutional neural network. doi: 10.15598/aeee.v15i3.2202
[25] Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., ... & Phung, D. (2017, October). Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. In 2017 IEEE international conference on data science and advanced Analytics (DSAA) (pp. 40-49). IEEE. doi: 10.1109/DSAA.2017.31

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Published

2021-09-30

How to Cite

Collazos Huertas, D. F., Gómez Gómez, G. S., & Álvarez Meza, A. M. (2021). Image-based Animal Recognition based on Transfer Learning. Scientia Et Technica, 26(03), 406–411. https://doi.org/10.22517/23447214.24538

Issue

Vol. 26 No. 03 (2021): Julio-Septiembre 2021

Section

Bioingeniería

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