메뉴 건너뛰기




Volumn , Issue , 2011, Pages 471-478

Sparse representation or collaborative representation: Which helps face recognition?

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION RESULTS; FACE CLASSIFICATION; FIRST CODE; LINEAR COMBINATIONS; REGULARIZED LEAST SQUARES; SPARSE REPRESENTATION; TESTING SAMPLES; TRAINING SAMPLE; WORKING MECHANISMS;

EID: 84863011302     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126277     Document Type: Conference Paper
Times cited : (2151)

References (30)
  • 1
    • 0030779611 scopus 로고    scopus 로고
    • Sparse coding with an overcomplete basis set: A strategy employed by V1?
    • B. Olshausen and D. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23):3311-3325, 1997.
    • (1997) Vision Research , vol.37 , Issue.23 , pp. 3311-3325
    • Olshausen, B.1    Field, D.2
  • 2
    • 33750383209 scopus 로고    scopus 로고
    • The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation
    • M. Aharon, M. Elad, and A. M. Bruckstein. The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE SP, 54(11):4311-4322, 2006.
    • (2006) IEEE SP , vol.54 , Issue.11 , pp. 4311-4322
    • Aharon, M.1    Elad, M.2    Bruckstein, A.M.3
  • 4
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the LASSO
    • R. Tibshirani. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society B, 58(1):267-288, 1996.
    • (1996) Journal of the Royal Statistical Society B , vol.58 , Issue.1 , pp. 267-288
    • Tibshirani, R.1
  • 5
    • 33646365077 scopus 로고    scopus 로고
    • For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution
    • D. Donoho. For Most Large Underdetermined Systems of Linear Equations the Minimal l1-Norm Solution is also the Sparsest Solution. Comm. Pure and Applied Math., 59(6):797-829, 2006.
    • (2006) Comm. Pure and Applied Math. , vol.59 , Issue.6 , pp. 797-829
    • Donoho, D.1
  • 7
    • 84946077141 scopus 로고    scopus 로고
    • Sparse representation for signal classification
    • K. Huang and S. Aviyente. Sparse representation for signal classification. In NIPS, 2006.
    • (2006) NIPS
    • Huang, K.1    Aviyente, S.2
  • 8
    • 61549128441 scopus 로고    scopus 로고
    • Robust face recognition via sparse representation
    • J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE PAMI, 31(2):210-227, 2009.
    • (2009) IEEE PAMI , vol.31 , Issue.2 , pp. 210-227
    • Wright, J.1    Yang, A.Y.2    Ganesh, A.3    Sastry, S.S.4    Ma, Y.5
  • 9
    • 84857008614 scopus 로고    scopus 로고
    • Kernel sparse representation for image classification and face recognition
    • S. H. Gao, I. W-H. Tsang, and L-T. Chia. Kernel Sparse Representation for Image Classification and Face Recognition. In ECCV, 2010.
    • (2010) ECCV
    • Gao, S.H.1    Tsang, I.W.-H.2    Chia, L.-T.3
  • 10
    • 78651092283 scopus 로고    scopus 로고
    • Gabor feature based sparse representation for face recognition with gabor occlusion dictionary
    • M. Yang and L. Zhang. Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary. In ECCV, 2010.
    • (2010) ECCV
    • Yang, M.1    Zhang, L.2
  • 11
    • 77949722130 scopus 로고    scopus 로고
    • Learning with l1-graph for image analysis
    • B. Cheng, J. Yang, S. Yan, Y. Fu, and T. Huang. Learning with l1-graph for image analysis. IEEE IP, 19(4):858-866, 2010.
    • (2010) IEEE IP , vol.19 , Issue.4 , pp. 858-866
    • Cheng, B.1    Yang, J.2    Yan, S.3    Fu, Y.4    Huang, T.5
  • 12
    • 70450209196 scopus 로고    scopus 로고
    • Linear spatial pyramid matching using sparse coding for image classification
    • J. Yang, K. Yu, Y. Gong and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR 2009.
    • (2009) CVPR
    • Yang, J.1    Yu, K.2    Gong, Y.3    Huang, T.4
  • 14
    • 51949108630 scopus 로고    scopus 로고
    • Simultaneous image transformation and sparse representation recovery
    • J. Z. Huang, X. L. Huang, and D. Metaxas. Simultaneous image transformation and sparse representation recovery. In CVPR 2008.
    • (2008) CVPR
    • Huang, J.Z.1    Huang, X.L.2    Metaxas, D.3
  • 15
    • 70450162109 scopus 로고    scopus 로고
    • Towards a practical face recognition system: Robust registration and illumination by sparse representation
    • A. Wagner, J. Wright, A. Ganesh, Z.H. Zhou, and Y. Ma, Towards a practical face recognition system: Robust registration and illumination by sparse representation. In CVPR 2009.
    • (2009) CVPR
    • Wagner, A.1    Wright, J.2    Ganesh, A.3    Zhou, Z.H.4    Ma, Y.5
  • 16
    • 77956007151 scopus 로고    scopus 로고
    • RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images. Submitted to
    • Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma. RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images. Submitted to IEEE PAMI, 2010.
    • (2010) IEEE PAMI
    • Peng, Y.1    Ganesh, A.2    Wright, J.3    Xu, W.4    Ma, Y.5
  • 19
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkage-thresholding algorithm for linear inverse problems
    • A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM. J. Imaging Science, 2(1):183-202, 2009.
    • (2009) SIAM. J. Imaging Science , vol.2 , Issue.1 , pp. 183-202
    • Beck, A.1    Teboulle, M.2
  • 21
    • 33646796359 scopus 로고    scopus 로고
    • Homotopy continuation for sparse signal representation
    • D. Malioutove, M. Cetin, and A. Willsky. Homotopy continuation for sparse signal representation. In ICASSP, 2005.
    • (2005) ICASSP
    • Malioutove, D.1    Cetin, M.2    Willsky, A.3
  • 23
    • 0035363672 scopus 로고    scopus 로고
    • From few to many: Illumination cone models for face recognition under variable lighting and pose
    • A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE PAMI, 23(6):643-660, 2001.
    • (2001) IEEE PAMI , vol.23 , Issue.6 , pp. 643-660
    • Georghiades, A.1    Belhumeur, P.2    Kriegman, D.3
  • 24
    • 18144420071 scopus 로고    scopus 로고
    • Acquiring linear subspaces for face recognition under variable lighting
    • K. Lee, J. Ho, and D. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE PAMI, 27(5):684-698, 2005.
    • (2005) IEEE PAMI , vol.27 , Issue.5 , pp. 684-698
    • Lee, K.1    Ho, J.2    Kriegman, D.3
  • 27
    • 78149282850 scopus 로고    scopus 로고
    • Linear regression for face recognition
    • I. Naseem, R. Togneri, and M. Bennamoun. Linear regression for face recognition. IEEE PAMI, 32(11):2106-2112, 2010.
    • (2010) IEEE PAMI , vol.32 , Issue.11 , pp. 2106-2112
    • Naseem, I.1    Togneri, R.2    Bennamoun, M.3
  • 29
    • 80052904079 scopus 로고    scopus 로고
    • Are sparse representations really relevant for image classification?
    • R. Rigamonti, M. Brown and V. Lepetit. Are Sparse Representations Really Relevant for Image Classification? In CVPR 2011.
    • (2011) CVPR
    • Rigamonti, R.1    Brown, M.2    Lepetit, V.3
  • 30
    • 80052911858 scopus 로고    scopus 로고
    • Is face recognition really a compressive sensing problem?
    • Q. Shi, A. Eriksson, A. Hengel, C. Shen. Is face recognition really a compressive sensing problem? In CVPR 2011.
    • (2011) CVPR
    • Shi, Q.1    Eriksson, A.2    Hengel, A.3    Shen, C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.