Li-Jun Yang
ABSTRACT
The convolution neural network has proved to be an efficient method for the image processing. It can optimize the convolution of the convolutional neural network and the filter in the poolinglayer Function, and optimize the performance of the role, and the number of parameters. It can construct a certain structure of the convolution neural network, and then the image data set is classified and processed to obtain the image model of the desired classification result.
- Kodovsky J, Fridrich J, Holub V. Ensemble classifiers for steganalysis of digital media [J]. IEEE Trans on Information Forensics & Security, 2012, 7(2): 432-444Google Scholar
Digital Library
- IEEE Transactions on Pattern Analysis & Machine Intelligence.2012, 35(11):2765-2781.Google Scholar
- Peng X, Zhang L, Yi Z.Scalable Sparse Subspace Clustering. ComputerVision and Pattern Recognition. IEEE, 2013:430-437Google Scholar
- Xu J, Xu K, Chen K, Reweighted sparse subspace clustering. Computer Vision &Image Understanding.2015:25-37.Google Scholar
- Patel V M, Nguyen H V, Vidal R. Latent Space Sparse and Low-Rank Subspace Clustering. IEEE Journal of Selected Topics in Signal Processing. 2015, 9(4):691-701.Google Scholar
- Sainath T N, Mohamed A R, Kingsbury B, Deep convolutional neuralnetworks for LVCSR [C] Proc of IEEE International Conference nAcoustics, Speech and Signal Processing. 2013: 8614-8618.Google Scholar
- Pevný T, Bas P, Fridrich J. Steganalysis by subtractive pixel adjacency.Google Scholar
- matrix [J]. IEEE Trans on Information Forensics & Security, 2010, 5 (2): 215-224Google ScholarDigital Library
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