A new Sparse Coding Approach for Human Face and Action Recognition
Subject Areas : Image ProcessingMohsen Nikpoor 1 , Mohammad Reza Karami-Mollaei 2 , Reza Ghaderi 3
1 - Babol University Of Technology
2 - Babol University of Technology
3 - Shahid beheshti university
Keywords: Sparse Coding, Manifold Learning, Graph Regularization, Affinity, Image Representation, Image Classification,
Abstract :
Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the sparse coding method the images may be classified into the same class and devalue classification performance. In this paper, we propose an Affine Graph Regularized Sparse Coding approach for resolving this problem. We apply the sparse coding and graph regularized sparse coding approaches by adding the affinity constraint to the objective function to improve the recognition rate. Several experiments has been done on well-known face datasets such as ORL and YALE. The first experiment has been done on ORL dataset for face recognition and the second one has been done on YALE dataset for face expression detection. Both experiments have been compared with the basic approaches for evaluating the proposed method. The simulation results show that the proposed method can significantly outperform previous methods in face classification. In addition, the proposed method is applied to KTH action dataset and the results show that the proposed sparse coding approach could be applied for action recognition applications too.
M. Long, G. Ding, J. Wang, J. Sun, Y.Guo, and P. S. Yu,Transfer Sparse Coding for Robust Image Representation,IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2013.#
J.Zhang, D. Zhao, W. Gao, Group-based Sparse Representation for Image Restoration, DOI0.1109/TIP.2014.2323127, IEEE Transactions on Image Processing.#
H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In Advances in Neural Information Processing Systems 20, NIPS, 2006.#
J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proceedings of the International Conference on Machine Learning, ICML, 2009.#
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, 2009.#
S. Gao, I. W.-H. Tsang, L.-T. Chia, and P. Zhao. Local features are not lonely – laplacian sparse coding for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2010.#
J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2009.#
M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, and D. Cai. Graph regularizedsparse coding for image representation. IEEE Transactions on Image Processing, Vol. 20, No.5, 2011.#
Y. N. Liu, F. Wu, Z. H. Zhang, Y. T. Zhuang, and S. C. Yan. Sparse representation using nonnegative curds and whey. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2010.#
R.Ptucha and e.avakis, " LGE-KSVD: Robust Sparse Representation Classification", IEEE Transactions On Image Processing, Vol. 23, No. 4, 2014.#
Y. Wen, L. Zhang, K. M. von Deneen, L. He,Face recognition using discriminative locality preserving vectors, Digit. Signal Process, http://dx.doi.org/10.1016/j.dsp.2015.11.001, 2015.#
X. Jiangand J. Lai, "Sparse And Dense Hybrid Representation via Dictionary Decomposition for Face Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence. ,DOI 10.1109/TPAMI.2014.2359453#
M. Yang, L. Zhang, J. Yang, and D. Zhang. Robust sparse coding for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2011.#
Z. Lu, Y. Peng, Latent semantic learning with structured sparse representation for human action recognition, in: ICCV, 2011.#
M.Zheng, J.Bu, C.Chen, C.Wang, L.Zhang, G.Qiu and D.Cai ,Graph Regularized Sparse Coding for Image Representation,JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007.#
B. Quanz, J. Huan, and M. Mishra. Knowledge transfer with low-quality data: A feature extraction issue. IEEE Transactions on Knowledge and Data Engineering,Vol.24, No.10, 2012.#
Hazewinkel, Michiel, ed.(2001),Affine transformation, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4.#
R. Fletcher. Practical methods of optimization. Wiley-Interscience, 1987.#
M. Aharon, M. Elad, A. Bruckstein, and Y. Katz. K-svd: An algorithm for designingovercomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, Vol.57, No.11, 2006.#
M. Belkin and P. Niyogi. Laplacianeigenmaps and spectral techniques for embeddingand clustering. In Advances in Neural Information Processing Systems 15, NIPS, 2001.#
X.Lu, Y.Yuan and P.Yan, Alternatively Constrained Dictionary Learning for Image Superresolution, IEEE Transactions On Cybernetics, Vol. 44, No. 3, 2014.#
E. Cand`es and T. Tao, “Near-optimal signal recovery from random projections: niversal encoding strategies?” IEEE transactions on information theory, Vol. 52, No. 12, pp. 5406–5425, 2006.#
F. Samaria, A. Harter, Parameterisation of a Stochastic Model for Human Face Identification, Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, 1994.#
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. on PAMI, vol. 19, no. 7, pp. 711-720, July 1997.#
S. J. Pan, I.W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, Vol.22, No.2, PP.199–210, 2011.#
