cvpr cvpr2013 cvpr2013-442 cvpr2013-442-reference knowledge-graph by maker-knowledge-mining
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Author: Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Yuchen Guo, Philip S. Yu
Abstract: Sparse coding learns a set of basis functions such that each input signal can be well approximated by a linear combination of just a few of the bases. It has attracted increasing interest due to its state-of-the-art performance in BoW based image representation. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different visual words of the codebook and encoded with different representations, which may severely degrade classification performance. In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately. Specifically, we aim to minimize the distribution divergence between the labeled and unlabeled images, and incorporate this criterion into the objective function of sparse coding to make the new representations robust to the distribution difference. Experiments show that TSC can significantly outperform state-ofthe-art methods on three types of computer vision datasets.
[1] M. Aharon, M. Elad, A. Bruckstein, and Y. Katz. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(1 1), 2006. 2, 4
[2] M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 15, NIPS, 2001. 2
[3] D. Cai, X. He, and J. Han. Spectral regression: A unified approach for sparse subspace learning. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM, 2007. 5
[4] D. Cai, X. He, J. Han, and T. S. Huang. Graph regularized nonnegative matrix factorization for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8): 1548–1560, 2011. 5
[5] R. Fletcher. Practical methods of optimization. Wiley-Interscience, 1987. 4
[6] 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. 1, 2, 3
[7] A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Scholkopf, and A. J. Smola. A kernel method for the two-sample problem. In Advances in Neural Information Processing Systems 20, NIPS, 2006. 2, 3
[8] K. Huang and S. Aviyente. Sparse representation for signal classification. In Advances in Neural Information Processing Systems 21, NIPS, 2007. 1
[9] H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In Advances in Neural Information Processing Systems 20, NIPS, 2006. 1, 2, 3, 4, 5, 6
[10] 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. 1, 2
[11] 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. 1, 2, 3, 4, 5
[12] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In Advances in Neural Information Processing Systems 23, NIPS, 2009. 1 –
[13] S. J. Pan, J. T. Kwok, and Q. Yang. Transfer learning via dimensionality reduction. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence, AAAI, 2008. 1, 3
[14] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2): 199–210,
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24] 2011. 1, 3, 6 S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22: 1345–1359, 2010. 1, 2 B. Quanz, J. Huan, and M. Mishra. Knowledge transfer with low-quality data: A feature extraction issue. In Proceedings ofthe IEEEInternational Conference on Data Engineering, ICDE, 2011. 1, 2, 6 B. Quanz, J. Huan, and M. Mishra. Knowledge transfer with low-quality data: A feature extraction issue. IEEE Transactions on Knowledge and Data Engineering, 24(10), 2012. 1, 2, 6 A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http : / /www .vlfeat . org/, 2008. 5 C. Wang, D. Blei, and L. Fei-Fei. Simultaneous image classification and annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2009. 5 J. J. Wang, J. C. Yang, K. Yu, F. J. Lv, T. Huang, and Y. H. Gong. Localityconstrained linear coding for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2010. 1, 2 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, 3 1(2), 2009. 1 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. 1 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. 1 M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, and D. Cai. Graph regularized sparse coding for image representation. IEEE Transactions on Image Processing, 20(5), 2011. 1, 2, 3, 4, 5, 6 444 111 242