publication . Conference object . Preprint . 2016

concatenated image completion via tensor augmentation and completion

Bengua, Johann A.; Tuan, Hoang D.; Phien, Ho N.; Do, Minh N.;
Open Access
  • Published: 13 Jul 2016
  • Publisher: IEEE
Abstract
Comment: 7 pages, 6 figures, submitted to ICSPCS 2016
Subjects
ACM Computing Classification System: ComputingMethodologies_COMPUTERGRAPHICSMathematicsofComputing_NUMERICALANALYSISComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Grayscale, Concatenation, Tensor completion, Tensor train, Curse of dimensionality, Algorithm, Matrix decomposition, Tensor, Computer science, Color image, Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Data Structures and Algorithms
19 references, page 1 of 2

[1] E. Candès and B. Recht, “Exact matrix completion via convex optimization,” Found. Comput. Math., vol. 9, no. 6, pp. 717-772, 2009.

[2] Z. Wen, W. Yin, and Y. Zhang, “Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm,” Mathematical Programming Computation, vol. 4, no. 4, pp. 333-361, 2012.

[3] J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, 2010.

[4] Y. Chen and Y. Chi, “Robust spectral compressed sensing via structured matrix completion,” IEEE Transactions on Information Theory, vol. 60, no. 10, pp. 6576-6601, Oct 2014.

[5] P. J. Shin, P. E. Z. Larson, M. A. Ohliger, M. Elad, J. M. Pauly, D. B. Vigneron, and M. Lustig, “Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion,” Magnetic Resonance in Medicine, vol. 72, no. 4, pp. 959-970, 2014.

[6] A. Kapur, K. Marwah, and G. Alterovitz, “Gene expression prediction using low-rank matrix completion,” BMC Bioinformatics, vol. 17, no. 1, pp. 1-13, 2016.

[7] Y. Luo, T. Liu, D. Tao, and C. Xu, “Multiview matrix completion for multilabel image classification,” IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2355-2368, Aug 2015.

[8] T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review, vol. 51, no. 3, pp. 455-500, 2009.

[9] M. Vasilescu and D. Terzopoulos, “Multilinear subspace analysis of image ensembles,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, June 2003, pp. 93-99.

[10] J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z. Chen, “Cubesvd: A novel approach to personalized web search,” in Proc. 14th Int'l World Wide Web Conf. (WWW ', 05), 2005, pp. 382-390. [OpenAIRE]

[11] T. Franz, A. Schultz, S. Sizov, and S. Staab, “Triplerank: Ranking semantic web data by tensor decomposition,” in The Semantic Web - ISWC 2009. Springer Berlin Heidelberg, 2009, vol. 5823, pp. 213- 228.

[12] Y. Xu, R. Hao, W. Yin, and Z. Su, “Parallel matrix factorization for low-rank tensor completion,” IPI, vol. 9, no. 2, pp. 601-624, Mar 2015.

[13] J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 208-220, Jan 2013.

[14] S. Gandy, B. Recht, and I. Yamada, “Tensor completion and low-n-rank tensor recovery via convex optimization,” Inv. Probl., vol. 27, no. 2, p. 025010, Jan 2011.

[15] J. A. Bengua, H. N. Phien, H. D. Tuan, and M. N. Do, “Efficient tensor completion for color image and video recovery: Low-rank tensor train,” arXiv:1606.01500, 2016. [Online]. Available: https://arxiv.org/abs/1606.01500 [OpenAIRE]

19 references, page 1 of 2
Abstract
Comment: 7 pages, 6 figures, submitted to ICSPCS 2016
Subjects
ACM Computing Classification System: ComputingMethodologies_COMPUTERGRAPHICSMathematicsofComputing_NUMERICALANALYSISComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Grayscale, Concatenation, Tensor completion, Tensor train, Curse of dimensionality, Algorithm, Matrix decomposition, Tensor, Computer science, Color image, Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Data Structures and Algorithms
19 references, page 1 of 2

[1] E. Candès and B. Recht, “Exact matrix completion via convex optimization,” Found. Comput. Math., vol. 9, no. 6, pp. 717-772, 2009.

[2] Z. Wen, W. Yin, and Y. Zhang, “Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm,” Mathematical Programming Computation, vol. 4, no. 4, pp. 333-361, 2012.

[3] J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, 2010.

[4] Y. Chen and Y. Chi, “Robust spectral compressed sensing via structured matrix completion,” IEEE Transactions on Information Theory, vol. 60, no. 10, pp. 6576-6601, Oct 2014.

[5] P. J. Shin, P. E. Z. Larson, M. A. Ohliger, M. Elad, J. M. Pauly, D. B. Vigneron, and M. Lustig, “Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion,” Magnetic Resonance in Medicine, vol. 72, no. 4, pp. 959-970, 2014.

[6] A. Kapur, K. Marwah, and G. Alterovitz, “Gene expression prediction using low-rank matrix completion,” BMC Bioinformatics, vol. 17, no. 1, pp. 1-13, 2016.

[7] Y. Luo, T. Liu, D. Tao, and C. Xu, “Multiview matrix completion for multilabel image classification,” IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2355-2368, Aug 2015.

[8] T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review, vol. 51, no. 3, pp. 455-500, 2009.

[9] M. Vasilescu and D. Terzopoulos, “Multilinear subspace analysis of image ensembles,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, June 2003, pp. 93-99.

[10] J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z. Chen, “Cubesvd: A novel approach to personalized web search,” in Proc. 14th Int'l World Wide Web Conf. (WWW ', 05), 2005, pp. 382-390. [OpenAIRE]

[11] T. Franz, A. Schultz, S. Sizov, and S. Staab, “Triplerank: Ranking semantic web data by tensor decomposition,” in The Semantic Web - ISWC 2009. Springer Berlin Heidelberg, 2009, vol. 5823, pp. 213- 228.

[12] Y. Xu, R. Hao, W. Yin, and Z. Su, “Parallel matrix factorization for low-rank tensor completion,” IPI, vol. 9, no. 2, pp. 601-624, Mar 2015.

[13] J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 208-220, Jan 2013.

[14] S. Gandy, B. Recht, and I. Yamada, “Tensor completion and low-n-rank tensor recovery via convex optimization,” Inv. Probl., vol. 27, no. 2, p. 025010, Jan 2011.

[15] J. A. Bengua, H. N. Phien, H. D. Tuan, and M. N. Do, “Efficient tensor completion for color image and video recovery: Low-rank tensor train,” arXiv:1606.01500, 2016. [Online]. Available: https://arxiv.org/abs/1606.01500 [OpenAIRE]

19 references, page 1 of 2
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publication . Conference object . Preprint . 2016

concatenated image completion via tensor augmentation and completion

Bengua, Johann A.; Tuan, Hoang D.; Phien, Ho N.; Do, Minh N.;