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YUV空间中基于稀疏自动编码器的无监督特征学习

李祖贺 樊养余 王凤琴

李祖贺, 樊养余, 王凤琴. YUV空间中基于稀疏自动编码器的无监督特征学习[J]. 电子与信息学报, 2016, 38(1): 29-37. doi: 10.11999/JEIT150557
引用本文: 李祖贺, 樊养余, 王凤琴. YUV空间中基于稀疏自动编码器的无监督特征学习[J]. 电子与信息学报, 2016, 38(1): 29-37. doi: 10.11999/JEIT150557
LI Zuhe, FAN Yangyu, WANG Fengqin. Unsupervised Feature Learning with Sparse Autoencoders in YUV Space[J]. Journal of Electronics and Information Technology, 2016, 38(1): 29-37. doi: 10.11999/JEIT150557
Citation: LI Zuhe, FAN Yangyu, WANG Fengqin. Unsupervised Feature Learning with Sparse Autoencoders in YUV Space[J]. Journal of Electronics and Information Technology, 2016, 38(1): 29-37. doi: 10.11999/JEIT150557

YUV空间中基于稀疏自动编码器的无监督特征学习

doi: 10.11999/JEIT150557
基金项目: 

陕西省科技统筹创新工程重点实验室项目(2013SZS15- K02)

Unsupervised Feature Learning with Sparse Autoencoders in YUV Space

Funds: 

The Science and Technology Innovation Engineering Program for Shaanxi Provincial Key Laboratories (2013SZS15-K02)

  • 摘要: 现有无监督特征学习算法通常在RGB色彩空间进行特征提取,而图像和视频压缩编码标准则广泛采用YUV色彩空间。为了利用人类视觉特性和避免色彩空间转换所消耗的计算量,该文提出一种基于稀疏自动编码器在YUV色彩空间进行无监督特征学习的方法。首先在YUV空间随机采集图像子块并进行白化处理,然后利用稀疏自动编码器进行无监督局部特征学习。在预处理阶段,针对YUV空间亮度和色度通道相互独立的特性,提出一种将亮度和色度进行分离的白化措施。最后用学习到的局部特征在大尺寸图像上进行卷积操作从而获得全局特征,并送入图像分类系统进行性能测试。实验结果表明:只要对亮度分量进行适当的白化处理,在YUV空间中的无监督特征学习就能够获得相当于甚至优于RGB空间的彩色图像分类性能。
  • [1] BENGIO Y, COURVILLE A, and VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
    [2] COATES A, NG A Y, and LEE H. An analysis of single-layer networks in unsupervised feature learning[C]. Preceedings of the 14th International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, 2011: 215-223.
    [3] KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Preceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, 2012: 1097-1105.
    [4] MASCI J, Meier U, CIREAN D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[C]. Preceedings of the 21st International Conference on Artificial Neural Networks, Espoo, 2011: 52-59.
    [5] LI Z, FAN Y, and LIU W. The effect of whitening transformation on pooling operations in convolutional autoencoders[J]. EURASIP Journal on Advances in Signal Processing, 2015, 2015(1): 1-11.
    [6] VINCENT P, LAROCHELLE H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. The Journal of Machine Learning Research, 2010, 11(Dec): 3371-3408.
    [7] YIN H, JIAO X, CHAI Y, et al. Scene classification based on single-layer SAE and SVM[J]. Expert Systems with Applications, 2015, 42(7): 3368-3380.
    [8] ZHANG F, DU B, and ZHANG L. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2175-2184.
    [9] LNGKVIST M and LOUTFI A. Learning feature representations with a cost-relevant sparse autoencoder[J]. International Journal of Neural Systems, 2015, 25(1): 1-11.
    [10] LIU H L, Taniguchi T, TAKANO T, et al. Visualization of driving behavior using deep sparse autoencoder[C]. Preceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn, 2014: 1427-1434.
    [11] SERMANET P, KAVUKCUOGLU K, CHINTALA S, et al. Pedestrian detection with unsupervised multi-stage feature learning[C]. Proceedings of Computer Vision and Pattern Recognition (CVPR), Portland, 2013: 3626-3633.
    [12] SHIN H C, ORTON M R, COLLINS D J, et al. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1930-1943.
    [13] SANKARAN A, PANDEY P, VATSA M, et al. On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders[C]. Proceedings of the 2014 IEEE International Joint Conference on Biometrics (IJCB), Clearwater, 2014: 1-7.
    [14] 孙志军, 薛磊, 许阳明. 基于深度学习的边际 Fisher 分析特征提取算法[J]. 电子与信息学报, 2013, 35(4): 805-811. doi:  10.3724/SP.J1146.2012.00949.
    [15] SUN Zhijun, XUE Lei, and XU Yangming. Marginal Fisher feature extraction algorithm based on deep learning[J]. Journal of Electronics Information Technology, 2013, 35(4): 805-811. doi:  10.3724/SP.J1146.2012.00949.
    [16] 刘高平, 赵杜娟, 黄华. 基于自编码神经网络重构的车牌数字识别[J]. 光电子激光, 2011, 22(1): 144-148.
    [17] LIU Gaoping, ZHAO Dujuan, and HUANG Hua. Character recognition of license plate using autoencoder neural network reconstruction[J]. Journal of OptoelectronicsLaser, 2011, 22(1): 144-148.
    [18] SUDHIR R and BABOO L D S S. An efficient CBIR technique with YUV color space and texture features[J]. Computer Engineering and Intelligent Systems, 2011, 2(6): 85-95.
    [19] BELL A J and SEJNOWSKI T J. Edges are the independent components of natural scenes[C]. Proceedings of the 10th Annual Conference on Neural Information Processing Systems (NIPS), Denver, 1997: 831-837.
    [20] 郑歆慰, 胡岩峰, 孙显, 等. 基于空间约束多特征联合稀疏编码的遥感图像标注方法研究[J]. 电子与信息学报, 2014, 36(8): 1891-1898. doi:  10.3724/SP.J1146.2013.01433.
    [21] ZHENG Xinwei, HU Yanfeng, SUN Xian, et al. Annotation of remote sensing images using spatial constrained multi-feature joint sparse coding[J]. Journal of Electronics Information Technology, 2014, 36(8): 1891-1898. doi:  10.3724/SP.J1146.2013.01433.
    [22] CIRESAN D C, MEIER U, MASCI J, et al. Flexible, high performance convolutional neural networks for image classificationC]. Proceedings of the 22nd?International Joint Conference on Artificial Intelligence, Barcelona, 2011: 1237-1242.
    [23] BOUREAU Y L, PONCE J, and lEcUN Y. A theoretical analysis of feature pooling in visual recognition[C]. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, 2010: 111-118.
    [24] ZENG R, WU J, SHAO Z, et al. Quaternion softmax classifier[J]. Electronics Letters, 2014, 50(25): 1929-1930.?
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    出版历程
    • 收稿日期:  2015-05-11
    • 修回日期:  2015-08-25
    • 刊出日期:  2016-01-19

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