• Laser & Optoelectronics Progress
  • Vol. 57, Issue 22, 221012 (2020)
Wei Lü1, Desheng Li1、*, Lang Tan2, Peiguang Jing1, and Yuting Su1
Author Affiliations
  • 1School of Electronics and Information Engineering, Tianjin University, Tianjin 300072, China
  • 2Beijing Smartchip Microelectronics Technology Co., Ltd., Beijing 102200, China
  • show less
    DOI: 10.3788/LOP57.221012 Cite this Article Set citation alerts
    Wei Lü, Desheng Li, Lang Tan, Peiguang Jing, Yuting Su. Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221012 Copy Citation Text show less
    References

    [1] Zhang M L, Zhou Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 26, 1819-1837(2014). http://ieeexplore.ieee.org/document/6471714

    [2] Boutell M R, Luo J B, Shen X P et al. Learning multi-label scene classification[J]. Pattern Recognition, 37, 1757-1771(2004). http://www.sciencedirect.com/science/article/pii/S0031320304001074

    [3] Read J, Pfahringer B, Holmes G et al. Classifier chains for multi-label classification[J]. Machine Learning, 85, 333-359(2011).

    [4] Zhang Q W, Zhong Y, Zhang M L. Feature-induced labeling information enrichment for multi-label learning[C]∥ 32th AAAI Conference on Artificial Intelligence, February 2-7, 2018, New Orleans, Louisiana, USA. Reston,, 4446-4453(2018).

    [5] Nie L Q, Wang X, Zhang J L et al. Enhancing micro-video understanding by harnessing external sounds[C]∥Proceedings of the 2017 ACM on Multimedia Conference-MM ’17, October 19-27, 2017. Mountain View, California, USA., 1192-1200(2017).

    [6] Lian Q S, Xia C C. Compressed sensing of color images based on local Gaussian model in the dual-tree complex wavelet[J]. Laser & Optoelectronics Progress, 48, 101001(2011).

    [7] Yang P, Liu D E, Li R X et al. Damage detection of metal parts by combining information entropy and low-rank tensor analysis[J]. Laser & Optoelectronics Progress, 56, 211006(2019).

    [8] Niu Q, Chen X H. Image recognition using joint projection learning algorithm based on latent low-rank representation[J]. Laser & Optoelectronics Progress, 56, 141006(2019).

    [9] Zhang J, Fu J P, Li X H. Low-rank regularized heterogeneous tensor decomposition algorithm for subspace clustering[J]. Laser & Optoelectronics Progress, 55, 071003(2018).

    [10] Yang P, Liu D E, Li R X et al. Damage detection of metal parts by combining information entropy and low-rank tensor analysis[J]. Laser & Optoelectronics Progress, 56, 211006(2019).

    [11] Zhang X H, Hao R F, Li T Y. Hyperspectral abnormal target detection based on low rank and sparse matrix decomposition-sparse representation[J]. Laser & Optoelectronics Progress, 56, 042801(2019).

    [12] Hassannejad H, Matrella G, Ciampolini P et al. Food image recognition using very deep convolutional networks[C]∥Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management-MADiMa ’16, October 16, 2016, Amsterdam, The Ne, 41-49(2016).

    [13] Wang L M, Qiao Y, Tang X O. Action recognition with trajectory-pooled deep-convolutional descriptors[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 4305-4314(2015).

    [14] Jia Z L, Zhang X, Guan N Y et al. Gene ranking of RNA-seq data via discriminant non-negative matrix factorization[J]. PLoS One, 10, e0137782(2015).

    [15] Liu G C, Yan S C. Latent Low-Rank Representation for subspace segmentation and feature extraction[C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain., 1615-1622(2011).

    [16] Zhu Y, Kwok J T, Zhou Z H. Multi-label learning with global and local label correlation[J]. IEEE Transactions on Knowledge and Data Engineering, 30, 1081-1094(2018).

    [17] Zhang M L, Zhou Z H. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 40, 2038-2048(2007). http://dl.acm.org/citation.cfm?id=1234417.1234635

    [18] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1-9(2015).

    [19] Tran D, Bourdev L, Fergus R et al. Learning spatiotemporal features with 3D convolutional networks[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 4489-4497(2015).

    [20] Yeh C K, Wu W C, Ko W J et al[C]. Learning deep latent spaces for multi-label classification 31th AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California. Reston,, 2838-2844(2017).

    Wei Lü, Desheng Li, Lang Tan, Peiguang Jing, Yuting Su. Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221012
    Download Citation