Fetching the paper…
Reading the bibliography…
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the em algorithm,” Journal of the Royal Statistical Society. Series B (Methodological) , pp. 1–38, 1977
1977
Earlier work this paper cites.
C. Berg, J. P. R. Christensen, and P. Ressel, Harmonic Analysis on Semigroups . Springer, 1984
1984
Earlier work this paper cites.
N. J. Higham, “Computing a nearest symmetric positive semidefinite matrix,” Linear Algebra and its Applications , vol. 103, pp. 103–118, 1988
1988
Earlier work this paper cites.
B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature , vol. 381, no. 6583, pp. 607–609, 1996
1996
Earlier work this paper cites.
T. Randen and J. H. Husøy, “Filtering for texture classification: A comparative study,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 21, no. 4, pp. 291–310, 1999
1999
Earlier work this paper cites.
P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 22, no. 10, pp. 1090–1104, 2000
2000
Earlier work this paper cites.
B. Schölkopf, “The kernel trick for distances,” in Proc. Advances in Neural Information Processing Systems (NIPS) , 2001, pp. 301–307
2001
Earlier work this paper cites.
P. J. Moreno, P. P. Ho, and N. Vasconcelos, “A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications,” in Proc. Advances in Neural Information Processing Systems (NIPS) , 2003
2003
Earlier work this paper cites.
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis . Cambridge University Press, 2004
2004
Earlier work this paper cites.
Z. Wang and B. C. Vemuri, “An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2004, pp. I–228
2004
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. Journal of Computer Vision (IJCV) , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . Cambridge University Press, 2004
2004
Earlier work this paper cites.
M. Hein and O. Bousquet, “Hilbertian metrics and positive definite kernels on probability measures,” in Proc. Int. Conf. Artificial Intelligence & Statistics , 2005, pp. 136–143
2005
Earlier work this paper cites.
N. Dalal and W. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2005, pp. 886–893
2005
Earlier work this paper cites.
O. Tuzel, F. Porikli, and P. Meer, “Region covariance: A fast descriptor for detection and classification,” in Proc. European Conference on Computer Vision (ECCV) . Springer, 2006, pp. 589–600
2006
Earlier work this paper cites.
X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” Int. Journal of Computer Vision (IJCV) , vol. 66, no. 1, pp. 41–66, 2006
2006
Earlier work this paper cites.
M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing , vol. 54, no. 11, pp. 4311–4322, 2006
2006
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning . Springer, 2006
2006
Earlier work this paper cites.
H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proc. Advances in Neural Information Processing Systems (NIPS) , 2007, pp. 801–808
2007
Earlier work this paper cites.
R. Bhatia, Positive Definite Matrices . Princeton University Press, 2007
2007
Earlier work this paper cites.
M. Müller, T. Röder, M. Clausen, B. Eberhardt, B. Krüger, and A. Weber, “Documentation: Mocap database HDM05,” Universität Bonn, Tech. Rep. CG-2007-2, 2007
2007
Cited alongside, same era.
A. Ess, B. Leibe, and L. V. Gool, “Depth and appearance for mobile scene analysis,” in Proc. Int. Conference on Computer Vision (ICCV) , 2007, pp. 1–8
2007
Cited alongside, same era.
Y. Pang, Y. Yuan, and X. Li, “Gabor-based region covariance matrices for face recognition,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 18, no. 7, pp. 989–993, 2008
2008
Cited alongside, same era.
O. Tuzel, F. Porikli, and P. Meer, “Pedestrian detection via classification on Riemannian manifolds,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 10, pp. 1713–1727, 2008
2008
Cited alongside, same era.
X. Wu, D. Xu, L. Duan, and J. Luo, “Action recognition using context and appearance distribution features,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2011, pp. 489–496
2011
Later among the works it cites.
M. T. Harandi, C. Sanderson, R. Hartley, and B. C. Lovell, “Sparse coding and dictionary learning for symmetric positive definite matrices: A kernel approach,” in Proc. European Conference on Computer Vision (ECCV) . Springer, 2012, pp. 216–229
2012
Later among the works it cites.
R. Caseiro, J. Henriques, P. Martins, and J. Batista, “Semi-intrinsic mean shift on Riemannian manifolds,” in Proc. European Conference on Computer Vision (ECCV) . Springer, 2012, pp. 342–355
2012
Later among the works it cites.
J. M. Lee, Introduction to smooth manifolds , ser. Graduate Texts in Mathematics. Springer, 2012, vol. 218
2012
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Rodriguez, J. Ahmed, and M. Shah, “Action MACH a spatio-temporal maximum average correlation height filter for action recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2008, pp. 1–8
2008
Cited alongside, same era.
J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 31, no. 2, pp. 210–227, 2009
2009
Cited alongside, same era.
B. Kulis, M. A. Sustik, and I. S. Dhillon, “Low-rank kernel learning with Bregman matrix divergences,” Journal of Machine Learning Research (JMLR) , vol. 10, pp. 341–376, 2009
2009
Cited alongside, same era.
Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti, “Similarity-based classification: Concepts and algorithms,” Journal of Machine Learning Research (JMLR) , vol. 10, pp. 747–776, 2009
2009
Cited alongside, same era.
L. Sharan, R. Rosenholtz, and E. Adelson, “Material perception: What can you see in a brief glance?” Journal of Vision , vol. 9, no. 8, pp. 784–784, 2009
2009
Cited alongside, same era.
M. Varma and A. Zisserman, “A statistical approach to material classification using image patch exemplars,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 31, no. 11, pp. 2032–2047, 2009
2009
Cited alongside, same era.
W. R. Schwartz and L. S. Davis, “Learning discriminative appearance-based models using partial least squares,” in Brazilian Symposium on Computer Graphics and Image Processing , 2009, pp. 322–329
2009
Cited alongside, same era.
H. Wang, M. M. Ullah, A. Kläser, I. Laptev, and C. Schmid, “Evaluation of local spatio-temporal features for action recognition,” in British Machine Vision Conference (BMVC) , 2009
2009
Cited alongside, same era.
S. Sra, “A new metric on the manifold of kernel matrices with application to matrix geometric means,” in Proc. Advances in Neural Information Processing Systems (NIPS) , 2012, pp. 144–152
2012
Later among the works it cites.
M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.0 beta,” http://cvxr.com/cvx , Sep. 2012
2012
Later among the works it cites.
M. T. Harandi, C. Sanderson, A. Wiliem, and B. C. Lovell, “Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures,” in IEEE Workshop on Applications of Computer Vision (WACV) , 2012, pp. 433–439
2012
Later among the works it cites.
A. Sanin, C. Sanderson, M. Harandi, and B. Lovell, “Spatio-temporal covariance descriptors for action and gesture recognition,” in IEEE Workshop on Applications of Computer Vision (WACV) , 2013, pp. 103–110
2013
Later among the works it cites.
S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, “Kernel methods on the Riemannian manifold of symmetric positive definite matrices,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2013
2013
Later among the works it cites.
K. Guo, P. Ishwar, and J. Konrad, “Action recognition from video using feature covariance matrices,” IEEE Transactions on Image Processing , vol. 22, no. 6, pp. 2479–2494, 2013
2013
Later among the works it cites.
J. Ho, Y. Xie, and B. Vemuri, “On a nonlinear generalization of sparse coding and dictionary learning,” in Proc. Int. Conference on Machine Learning (ICML) , 2013, pp. 1480–1488
2013
Later among the works it cites.
A. Cherian, S. Sra, A. Banerjee, and N. Papanikolopoulos, “Jensen-Bregman logdet divergence with application to efficient similarity search for covariance matrices,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 9, pp. 2161–2174, 2013
2013
Later among the works it cites.
M. Harandi, C. Sanderson, C. Shen, and B. Lovell, “Dictionary learning and sparse coding on Grassmann manifolds: An extrinsic solution,” in Proc. Int. Conference on Computer Vision (ICCV) , 2013, pp. 3120–3127
2013
Later among the works it cites.
D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2013, pp. 3025–3032
2013
Later among the works it cites.
M. E. Hussein, M. Torki, M. A. Gowayyed, and M. El-Saban, “Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations,” in Proc. Int. Joint Conference on Artificial Intelligence (IJCAI) , 2013
2013
Later among the works it cites.
Z. Liao, J. Rock, Y. Wang, and D. Forsyth, “Non-parametric filtering for geometric detail extraction and material representation,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2013, pp. 963–970
2013
Later among the works it cites.
L. Bazzani, M. Cristani, and V. Murino, “Symmetry-driven accumulation of local features for human characterization and re-identification,” Computer Vision and Image Understanding (CVIU) , vol. 117, no. 2, pp. 130–144, 2013
2013
Later among the works it cites.
——, “Tensor sparse coding for positive definite matrices,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 36, no. 3, pp. 592–605, 2014
2014
Closest in time.
M. T. Harandi, M. Salzmann, and R. Hartley, “From manifold to manifold: geometry-aware dimensionality reduction for SPD matrices,” in Proc. European Conference on Computer Vision (ECCV) . Springer, 2014
2014
Closest in time.
A. Kovashka and K. Grauman, “Learning a hierarchy of discriminative space-time neighborhood features for human action recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2010, pp. 2046–2053
2053
Closest in time.