The student-t mixture as a natural image patch prior with application to image compression
暂无分享,去创建一个
[1] D. Anderson,et al. Algorithms for minimization without derivatives , 1974 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[4] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[5] Gregory K. Wallace,et al. The JPEG still picture compression standard , 1991, CACM.
[6] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[7] Geoffrey J. McLachlan,et al. Robust mixture modelling using the t distribution , 2000, Stat. Comput..
[8] Jan Skoglund,et al. Vector quantization based on Gaussian mixture models , 2000, IEEE Trans. Speech Audio Process..
[9] Touradj Ebrahimi,et al. The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..
[10] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[11] Vivek K. Goyal,et al. Theoretical foundations of transform coding , 2001, IEEE Signal Process. Mag..
[12] Geoffrey E. Hinton,et al. Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.
[13] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[14] Gerald Schaefer,et al. UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.
[15] Samuel Kotz,et al. Multivariate T-Distributions and Their Applications , 2004 .
[16] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[17] Robert M. Gray,et al. Lloyd clustering of Gauss mixture models for image compression and classification , 2005, Signal Process. Image Commun..
[18] Kun Huang,et al. A multiscale hybrid linear model for lossy image representation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[19] Michael Elad,et al. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.
[20] William T. Freeman,et al. What makes a good model of natural images? , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Michael Elad,et al. Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..
[22] Guillermo Sapiro,et al. Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[23] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[24] Guillermo Sapiro,et al. Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.
[25] Christine Guillemot,et al. Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary , 2011, IEEE Journal of Selected Topics in Signal Processing.
[26] Yair Weiss,et al. From learning models of natural image patches to whole image restoration , 2011, 2011 International Conference on Computer Vision.
[27] Matthias Bethge,et al. In All Likelihood, Deep Belief Is Not Enough , 2010, J. Mach. Learn. Res..
[28] William A. Pearlman,et al. Digital Signal Compression: Principles and Practice , 2011 .
[29] Yair Weiss,et al. "Natural Images, Gaussian Mixtures and Dead Leaves" , 2012, NIPS.
[30] M. Bethge,et al. Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations , 2011, PloS one.
[31] Stéphane Mallat,et al. Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity , 2010, IEEE Transactions on Image Processing.
[32] I. Horev,et al. Adaptive image compression using sparse dictionaries , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).
[33] Geoffrey E. Hinton,et al. Deep Mixtures of Factor Analysers , 2012, ICML.
[34] Hugo Larochelle,et al. RNADE: The real-valued neural autoregressive density-estimator , 2013, NIPS.
[35] Geoffrey E. Hinton,et al. Tensor Analyzers , 2013, ICML.
[36] Benjamin Schrauwen,et al. Learning a piecewise linear transform coding scheme for images , 2013, International Conference on Graphic and Image Processing.
[37] Hugo Larochelle,et al. A Deep and Tractable Density Estimator , 2013, ICML.