Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Novel Image Classification Method Based on Few-Shot Learning in Monkey Species

  • Wang, Guangxing (Information Technology Center, Jiujiang University) ;
  • Lee, Kwang-Chan (School of Computer Information & Communication Engineering, Kunsan National University) ;
  • Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
  • Received : 2021.03.02
  • Accepted : 2021.04.15
  • Published : 2021.06.30
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Abstract

This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.

Keywords

Acknowledgement

This research is partially supported by Institute of Information and Telecommunication Technology of KNU.

References

  1. L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and R. Qu, "A survey of deep learning-based object detection," IEEE Access, vol. 7, pp. 128837-128868, 2019. DOI: 10.1109/ACCESS.2019.2939201.
  2. H. Laga, L. V. Jospin, F. Boussaid, and M. Bennamoun, "A survey on deep learning techniques for stereo-based depth estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-27, 2020. DOI: 10.1109/TPAMI.2020.3032602.
  3. G. X. Wang and S. Y. Shin, "An improved text classification method for sentiment classification," Journal of Information and Communication Convergence Engineering, vol. 17, no. 1, pp. 41-48, 2019. DOI: 10.6109/jicce.2019.17.1.41.
  4. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. DOI: 10.1109/5.726791.
  5. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications on ACM, vol. 60, no. 6, pp. 84-90, 2017. DOI: 10.1145/3065386.
  6. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1-9, 2015.
  7. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proceedings of International Conference on Learning Representations, San Diego, CA, USA, 2014. DOI: arxiv.org/abs/1409.1556.
  8. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of Computer Vision & Pattern Recognition 2016, Las Vegas, NV, USA, pp. 770-778, 2016.
  9. W. Zhang, "Shift-invariant pattern recognition neural network and its optical architecture," in Proceedings of Annual Conference of The Japan Society of Applied Physics, Montreal, CA, 1988.
  10. J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, "ImageNet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami: FL, pp. 248-255, 2009. DOI: 10.1109/CVPR.2009.5206848.
  11. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "ImageNet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, pp. 211-252, 2015. DOI: 10.1007/s11263-015-0816-y.
  12. N. Bendre, H. T. Marin, and P. Najafirad, "Learning from few samples: A survey," Computer Vision and Pattern Recognition, pp. 1-17, 2007.
  13. J. Lu, P. Gong, J. Ye, and C. Zhang, "Learning from very few samples: A survey," Computer Vision and Pattern Recognition, pp. 1-30, 2020.
  14. Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, "Generalizing from a few examples: A survey on few-shot learning," ACM Computing Survey (CSUR), vol. 53, no. 3, pp. 1-34, 2020.
  15. Kaggle, 10 monkey species [Internet], Available: https://www.kaggle.com/slothkong/10-monkey-species.
  16. Kaggle, Dogs vs. cats [internet], Available: https://www.kaggle.com/salader/dogsvscats.
  17. D. P. Kingma and J. L. Ba, "Adam: A method for stochastic optimization," in The 3rd International Conference for Learning Representations, San Diego, CA, USA, pp. 1-15, 2015.