• Infrared and Laser Engineering
  • Vol. 47, Issue 8, 826004 (2018)
Chen Yu, Wen Xinling, Liu Zhaoyu, and Ma Pengge
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/irla201847.0826004 Cite this Article
    Chen Yu, Wen Xinling, Liu Zhaoyu, Ma Pengge. Research of multi-missile classification algorithm based on sparse auto-encoder visual feature fusion[J]. Infrared and Laser Engineering, 2018, 47(8): 826004 Copy Citation Text show less
    References

    [1] Cheng Peirui, Wang Jianli, Wang Bin, et al. Salient object detection based on multi-scale region contrast[J]. Chinese Optics, 2016, 9(1): 97-105. (in Chinese)

    [2] Shi Dongcheng, Ni Kang. Background modeling based on YCbCr color space and gesture shadow elimination[J]. Chinese Optics, 2015, 8(4): 589-595. (in Chinese)

    [3] Chen Chao, Yu Yanqin, Huang Shujun, et al. 3D small-field imaging system[J]. Infrared and Laser Engineering, 2016, 45(8): 0824002. (in Chinese)

    [4] Zhang Zhongyu, Jiao Shuhong. Infrared ship target detection method based on multiple feature fusion [J]. Infrared and Laser Engineering, 2015, 44(s): 29-34. (in Chinese)

    [5] Guo Congzhou, Shi Wenjun, Qin Zhiyuan, et al. Non-convex sparsity regularization for wave back restoration of space object images[J]. Optics and Precision Engineering, 2016, 24(4): 902-912. (in Chinese)

    [6] Yin Ming, Duan Puhong, Chu Biao, et al. Fusion of infrared and visible images combined with NSDTCT and sparse representation[J]. Optics and Precision Engineering, 2016, 24(7): 1763-1771. (in Chinese)

    [7] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Preceedings of the 26th Annual Conference on Neural Information Processing Systems(NIPS), 2012: 1097-1105.

    [8] Bengio Y, Clurville A, Vincent P. Representation learning: a review and new perspectives [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.

    [9] Masci J, Meier U, Dan C, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[C]//Proceedings of the 21st International Conference on Artificial Neural Networks, 2011: 52-59.

    [10] Li Zuhe, Fan Yangyu, Wang Fengqin. Unsupervised feature learning with sparse autoencoders in YUV space [J]. Journal of Electronics & Information Technology, 2016, 38(1): 29-37. (in Chinese)

    [11] Zhang F, Du B, Zhang L. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2175-2184.

    [12] Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 0502002. (in Chinese)

    [13] Adam Coates, Honglak Lee, Andrew Y Ng. An analysis of single-layer networks in unsupervised feature learning [C]// 14th International Conference on Artificial Intelligence and Statistics, 2011: 215-223.

    [14] Dan C Ciresan, Ueli Meier, Jonathan Masci, et al. Flexible, high performance convolutional neural networks for image classification[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 2011: 1237-1242.

    [15] Zeng R, Wu J, Shao Z, et al. Quaternion softmax classifier[J]. Electronics Letters, 2014, 50(25): 1929-1930.

    Chen Yu, Wen Xinling, Liu Zhaoyu, Ma Pengge. Research of multi-missile classification algorithm based on sparse auto-encoder visual feature fusion[J]. Infrared and Laser Engineering, 2018, 47(8): 826004
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