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A Review of Image Recognition with Deep Convolutional Neural Network

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10361)

Abstract

Image recognition technology is widely used in industry, space military, medicine and agriculture. At present, most of the image recognition methods use artificial feature extraction which is not only laborious, time consuming, but also difficult to do. Deep convolutional neural network is becoming a research hotspot in recent years. It has successfully applied to character recognition, face recognition, and so on. The traditional deep convolutional neural network still has some defaults when dealing with large-scale images and high-resolution complex images. So many research works are rolling ahead to improve the network to make it more efficient and robust. Firstly, the principle of the traditional convolutional neural network was briefly introduced. Then, the improvements on convolutional layer, pooling layer, activation function of convolutional neural network in recent years were summarized. Its applications in image recognition were also presented. Finally, the challenges in convolutional neural network research were analyzed and our recent works ware introduced.

Keywords

  • Convolutional neural network
  • Image recognition
  • Convolutional layer
  • Pooling layer
  • Activation function

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References

  1. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, p. 1150 (1999). doi:10.1109/ICCV.1999.790410

  2. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005). doi:10.1109/CVPR.2005.177

  3. Lecun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). doi:10.1162/neco.1989.1.4.541

    CrossRef  Google Scholar 

  4. Yu, K., Jia, L., Chen, Y., Xu, W.: Deep learning: yesterday, today, and tomorrow. J. Comput. Res. Dev. 50(9), 1799–1804 (2013). doi:10.7544/issn1000-1239.2013.20131180

    Google Scholar 

  5. LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). doi:10.1109/5.726791

    CrossRef  Google Scholar 

  6. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400, 2013

  7. Baldi, P., Zhiqin, L.: Complex-valued autoencoders. Neural Netw. 33(3), 136–147 (2012). doi:10.1016/j.neunet.2012.04.011

    CrossRef  MATH  Google Scholar 

  8. Zhang, W., Xu, Y., Ni, J., et al.: Image target recognitions method based on multi-scale block convolutional neural network. J. Comput. Appl. 1033–1038 (2016). doi:10.11772/j.issn.1001-9081.2016.04.1033

  9. Wang, G., Xu, J.: Fast feature representation method based on multi-level pyramid convolution neural network. Appl. Res. Comput. 32(8), 2492–2495 (2015). doi:10.3969/j.issn.1001-3695.2015.08.061

    Google Scholar 

  10. Simoncelli, E.P., Heeger, D.J.: A model of neuronal responses in visual area MT. Vis. Res. (1998). doi:10.1016/S0042-6989(97)00183-1

    Google Scholar 

  11. Hyvärinen, A., Köster, U.: Complex cell pooling and the statistics of natural images. Netw. Comput. Neural Syst. 18(2), 81–100 (2007). doi:10.1080/09548980701418942

    MathSciNet  CrossRef  Google Scholar 

  12. Bruna, J., Szlam, A., Lecun, Y.: Signal recovery from pooling representations. In: ICML (2014)

    Google Scholar 

  13. Gulcehre, C., Cho, K., Pascanu, R., Bengio, Y.: Learned-norm pooling for deep feedforward and recurrent neural networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS, vol. 8724, pp. 530–546. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44848-9_34

    Google Scholar 

  14. Yu, D., Wang, H., Chen, P., Wei, Z.: Mixed pooling for convolutional neural networks. Rough Sets Knowl. Technol. (2014). doi:10.1007/978-3-319-11740-9_34

  15. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  16. Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: Regularization of neural networks using dropconnect. In: ICML (2013)

    Google Scholar 

  17. Zeiler, M.D., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks. CoRR (2013)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). doi:10.1007/978-3-319-10578-9_23

    Google Scholar 

  19. Gulcehre, C., Moczulski, M., Denil, M., et al.: Noisy activation functions. In: International Conference on Machine Learning (2016)

    Google Scholar 

  20. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines Vinod Nair. In: ICML, pp. 807–814 (2014)

    Google Scholar 

  21. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics (2011)

    Google Scholar 

  22. Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)

  23. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv preprint arXiv:1502.01852 (2015). doi:10.1109/iccv.2015.123

  24. Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)

  25. Li, Y., Fan, C., Li, Y., et al.: Improving deep neural network with multiple parametric exponential linear units. arXiv preprint arXiv:1606.00305 (2016)

  26. Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)

    Google Scholar 

  27. LeCun, Y., Denker, J.S., Henderson, D., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, Colorado, USA, pp. 396–404 (1994)

    Google Scholar 

  28. LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http://yann.lecun.com/esdb/mnist

  29. Sermanet, P., Chintala, S., Lecun, Y.: Convolutional neural networks applied to house numbers digit classification. In: International Conference on Pattern Recognition, pp. 3288–3291. IEEE (2012)

    Google Scholar 

  30. Yang, Z., Tao, D., Zhang, S., et al.: Similar handwritten chinese character recognition based on deep neural networks with big data. J. Commun. 35(9), 184–189 (2014). doi:10.33969/.j.issn.1000-436x.2014.09.019

    Google Scholar 

  31. Zhao, Z., Yang, S., Ma, Z.: Lincese plate character recongnition based on convolutional neural network LeNet-5. J. Syst. Simul. 22(3), 638–641 (2010). doi:10.16182/j.cnki.joss.2010.03.040

    Google Scholar 

  32. Taigman, Y., Yang, M., Ranzato, M.A., et al.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701–1708 (2014). doi:10.1109/cvpr.2014.220

  33. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1891–1898. IEEE (2014). doi:10.1109/cvpr.2014.244

  34. Sun, Y., Chen, Y., Wang, X., et al.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)

    Google Scholar 

  35. Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265 (2014). doi:10.1109/cvpr.2015.7298907

  36. Sun, Y., Liang, D., Wang, X., et al.: DeepID3: face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873 (2015)

  37. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  38. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014). doi:10.1109/cvpr.2015.7298594

  39. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. arXiv preprint arXiv:1503.03832 (2015). doi:10.1109/cvpr.2015.7298682

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Acknowledgments

The authors thank The Natural Science Foundation of Hebei Provence for their financial support (F2015201033), the Natural Science Foundation of Hebei Provence for their financial support (F2017201069). The authors also thank Information Technology Center of Hebei University for providing the high performance computing platform.

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Correspondence to Wenzhu Yang .

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Liu, Q. et al. (2017). A Review of Image Recognition with Deep Convolutional Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_7

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