PCA预训练的卷积神经网络目标识别算法

doi:10.3969/j.issn.1001-2400.2016.03.028

Abstract

Abstract:

To improve the insufficiency of Synthetic Aperture Radar(SAR) labeled training data for Convolutional Neural Networks(CNN) and the recognition rate for large variations, a novel CNN recognition algorithm is proposed. Firstly, a set of features is extracted from the original data by unsupervised training based on PCA as the initial filter set for CNN. Secondly, in order to accelerate the training speed while avoiding over-fitting, the Rectified Linear Units(ReLU) is adopted as the non-linear function. Thirdly, to strengthen robustness and mitigate the defects of pooling upon features, a probabilistic max-pooling sampling method is introduced and local contrast normalization is exploited on features after the convolutional layer. Experiments demonstrate that our algorithm outperforms the original CNN in recognition rate and achieves better robustness for large variations and complex background.

Key words: convolutional neural network, principal component analysis, probabilistic max-pooling, rectified linear units, local contrast normalization

CLC Number:

TP391.41

SHI Hehuan;XU Yuelei;MA Shiping;LI Yueyun;LI Shuai. Convolutional neural networks recognition algorithm based on PCA[J].Journal of Xidian University, 2016, 43(3): 161-166.

References

［1］李帅, 许悦雷, 马时平, 等. 一种深度神经网络SAR遮挡目标识别方法［J］. 西安电子科技大学学报, 2015, 42(3): 154-160.
LI Shuai, XU Yuelei, MA Shiping, et al. New Method for SAR Occluded Targets Recognition Using DNN［J］. Journal of Xidian University, 2015, 42(3): 154-160.
［2］ KYIZHEUSKY A, SUTSKEVER I, HINTON G E, et al. Image Net Classification with Deep Convolutional Neural Networks［C］//Neural Information Processing Systems Conference. Nevada: Neural Information Processing Systems Foundation, 2012: 1106-1114.
［3］ DAN C, UELI M, JURGEN S. Multi-column Deep Neural Networks for Image Classification［C］//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2012: 3642-3649.
［4］ KAVUKCUOGLU K, SERMANET P, BOUREAU Y, et al. Learning Convolutional Feature Hierarchies for Visual Recognition［C］//Advances in Neural Information Processing Systems Workshops. Real Hook: Neural Information Processing System Foundation, 2010: 1090-1098.
［5］ KAVUKCUOGLU K, RANZATO M, FERGUS R, et al. Learning Invariant Features through Topographic Filter Maps［C］//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Computer Society, 2009: 1605-1612.
［6］ ZEILER M D, FERGUS R. Visualizing and Understanding Convolutional Neural Networks［C］//European Conference on Computer Vision. Berlin: Springer International Publishing, 2014: 818-833.
［7］ LEE H, GROSSE R, RANGANATH R, et al. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representation［C］//Proceedings of the 26th International Conference on Machine Learning. New York: ACM, 2009: 609-616.
［8］ BURGES C J C, PLATT J C, JANA S, et al. Distortion Discriminant Analysis for Audio Fingerprinting［J］. IEEE Transactions on Speech and Audio Processing, 2003, 11(3): 165-174.
［9］ LECUN Y, BOTTOU L, BENGIO Y. Gradient-based Learning Applied to Document Recognition［J］. Proceedings of the IEEE, 2008, 86 (11): 2278-2324.
［10］ NEUBAUER C. Evaluation of Convolutional Neural Networks for Visual Recognition［J］. IEEE Transactions on Neural Networks, 1998, 11(4): 685-696.
［11］ JARRETT K, KAVUKCUOGLU K, RANZATO M A, et al. What is the Best Multi-stage for Object Architecture?［C］//Proceedings of the IEEE 12th International Conference on Computer Vision. Piscataway: IEEE, 2009: 2146-2153.
［12］ BOUVRIE J. Notes on Convolutional Neural Networks ［EB/OL］. ［2014-11-24］. http://www.math.duke.edu/～jvb/papers/cnn_tutorial.pdf.

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Convolutional neural networks recognition algorithm based on PCA

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