Fast Image Recognition with Gabor Filter and Pseudoinverse Learning AutoEncoders
Deep neural network has been successfully used in various fields, and it has received significant results in some typical tasks, especially in computer vision. However, deep neural network are usually trained by using gradient descent based algorithm, which results in gradient vanishing and gradient explosion problems. And it requires expert level professional knowledge to design the structure of the deep neural network and find the optimal hyper parameters for a given task. Consequently, training a deep neural network becomes a very time consuming problem. To overcome the shortcomings mentioned above, we present a model which combining Gabor filter and pseudoinverse learning autoencoders. The method referred in model optimization is a non-gradient descent algorithm. Besides, we presented the empirical formula to set the number of hidden neurons and the number of hidden layers in the entire training process. The experimental results show that our model is better than existing benchmark methods in speed, at same time it has the comparative recognition accuracy also.
KeywordsPseudoinverse learning autoencoder Gabor filter Image recognition Handcraft feature
The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 61472043), the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the NSFC and CAS, and Natural Science Foundation of Shandong (ZR2015FL006). Prof. Ping Guo and Qian Yin are the authors to whom all correspondence should be addressed.
- 2.Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
- 3.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
- 4.Karen, S., Andrew, Z.: Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556[cs.CV] (2014)
- 6.Wang, K., Guo, P., Yin, Q., et al.: A pseudoinverse incremental algorithm for fast training deep neural networks with application to spectra pattern recognition. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3453–3460. IEEE (2016)Google Scholar
- 8.Gabor, D.: Theory of communication. J. Inst. Electr. Eng. I Gen. 93(26), 429–441 (1946)Google Scholar
- 12.Fazli, S., Afrouzian, R., Seyedarabi, H.: High-performance facial expression recognition using gabor filter and probabilistic neural network. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 93–96 (2009)Google Scholar
- 15.Guo, P., Chen, P.C.L., Sun, Y.: An exact supervised learning for a three-layer supervised neural network. In: Second International Conference on Neural Information Processing (ICONIP 1995), pp. 1041–1044 (1995)Google Scholar
- 16.Guo, P., Lyu, M.R., Mastorakis, N.E.: Pseudoinverse learning algorithm for feedforward neural networks. In: Advances in Neural Networks and Applications, pp. 321–326 (2001)Google Scholar
- 18.Wang, K., Guo, P., Xin, X., Ye, Z.: Autoencoder, low rank approximation and pseudoinverse learning algorithm. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 948–953. IEEE (2017)Google Scholar
- 20.Guo, P.: A VEST of the pseudoinverse learning algorithm. Preprint arXiv:1805.07828 (2018)