[1] Zhang J, Zhang T, Yang Z L et al. Vehicle model recognition method based on deep convolutional neural network[J]. Transducer and Microsystem Technologies, 35, 19-22(2016).

[2] Zheng H L, Fu J L, Mei T et al. Learning multi-attention convolutional neural network for fine-grained image recognition. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, Venice, Italy. New York: IEEE, 5219-5227(2017).

[3] Yan Y, Ni B, Yang X. Fine-grained recognition via attribute-guided attentive feature aggregation. [C]∥The 25th ACM International Conference on Multimedia, October 23-27, 2017, California, USA. New York: ACM, 1032-1040(2017).

[4] Huang K, Zhang B L. Fine-grained vehicle recognition by deep convolutional neural network. [C]∥2016 9th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics, October 15-17, Datong, China. New York: IEEE, 465-470(2016).

[5] Khusnuliawati H, Fatichah C, Soelaiman R. Multi-feature fusion using SIFT and LEBP for finger vein recognition[J]. Telecommunication Computing Electronics and Control, 15, 478-485(2017).

[6] Murtaza F, Yousaf M H, Velastin S A. Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description[J]. IET Computer Vision, 10, 758-767(2016). http://ieeexplore.ieee.org/document/7575435/

[7] Ling Y G, Hu W P. Vehicle type recognition based on SURF and integral channel features[J]. Video Engineering, 40, 139-143(2016).

[8] Fang J, Zhou Y, Yu Y et al. Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture[J]. IEEE Transactions on Intelligent Transportation Systems, 18, 1782-1792(2017). http://ieeexplore.ieee.org/document/7744550

[9] Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1798-1828(2013). http://ieeexplore.ieee.org/document/6472238

[10] Zou Y B, Zhou W L, Chen X Z. Research of laser vision seam detection and tracking system based on depth hierarchical feature[J]. Chinese Journal of Lasers, 44, 0402009(2017).

[11] Zhou Y C, Xu T Y, Zheng W et al. Classification and recognition approaches of tomato main organs based on DCNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 33, 219-226(2017).

[12] Song J, Kim H I, Yong M. Fast and robust face detection based on CNN in wild environment[J]. Journal of Korea Multimedia Society, 19, 1310-1319(2016).

[13] Liu B, Yu X C, Zhang P Q et al. A semi-supervised convolutional neural network for hyperspectral image classification[J]. Remote Sensing Letters, 8, 839-848(2017). http://www.tandfonline.com/doi/abs/10.1080/2150704X.2017.1331053

[14] Qu L, Wang K R, Chen L L et al. Fast road detection based on RGBD images and convolutional neural network[J]. Acta Optica Sinica, 37, 1010003(2017).

[15] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 20, 273-297(1995).

[16] Elleuch M, Maalej R, Kherallah M. A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition[J]. Procedia Computer Science, 80, 1712-1723(2016). http://dl.acm.org/citation.cfm?id=3109186

[17] Li X Q, Zhang Y, Liao D. Mining key skeleton poses with latent SVM for action recognition[J]. Applied Computational Intelligence and Soft Computing, 2017, 1-11(2017). http://dl.acm.org/citation.cfm?id=3058302

[18] Cheng L Y, Mi G Y, Li S et al. Quality diagnosis of joints in laser brazing based on principal component analysis-support vector machine model[J]. Chinese Journal of Lasers, 44, 0302004(2017).

[19] Huang F J. LeCun Y. Large-scale learning with SVM and convolutional for generic object categorization. [C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'06), June 17-22, New York, USA. New York: IEEE, 284-291(2006).

[20] Lapin M, Hein M, Schiele B. Loss functions for top-k error: analysis andinsights. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, Las Vegas, NV, USA. New York: IEEE, 1468-1477(2016).

[21] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015). http://dl.acm.org/citation.cfm?id=2846559"