激光与光电子学进展, 2020, 57 (22): 221012, 网络出版: 2020-11-12  

基于多视角低秩表征的短视频多标签学习模型 下载: 886次

Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation
作者单位
1 天津大学电气自动化与信息工程学院, 天津 300072
2 北京智芯微电子科技有限公司, 北京 102200
引用该论文

吕卫, 李德盛, 谭浪, 井佩光, 苏育挺. 基于多视角低秩表征的短视频多标签学习模型[J]. 激光与光电子学进展, 2020, 57(22): 221012.

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.

参考文献

[1] Zhang M L, Zhou Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.

[2] Boutell M R, Luo J B, Shen X P, et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37(9): 1757-1771.

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

[4] Zhang QW, ZhongY, Zhang ML. 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,VA: AIAA Press, 2018: 4446- 4453.

[5] Nie LQ, WangX, Zhang JL, 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.New York: ACM Press, 2017: 1192- 1200.

[6] 练秋生, 夏长城. 基于双树复数小波局部高斯模型的彩色图像压缩感知[J]. 激光与光电子学进展, 2011, 48(10): 101001.

    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, 2011, 48(10): 101001.

[7] 杨鹏, 刘德儿, 李瑞雪, 等. 结合信息熵与低秩张量分析的金属零件破损检测[J]. 激光与光电子学进展, 2019, 56(21): 211006.

    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, 2019, 56(21): 211006.

[8] 牛强, 陈秀宏. 基于隐式低秩表示的联合投影学习算法及图像识别[J]. 激光与光电子学进展, 2019, 56(14): 141006.

    Niu Q, Chen X H. Image recognition using joint projection learning algorithm based on latent low-rank representation[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141006.

[9] 张静, 付建鹏, 李新慧. 基于低秩正则化异构张量分解的子空间聚类算法[J]. 激光与光电子学进展, 2018, 55(7): 071003.

    Zhang J, Fu J P, Li X H. Low-rank regularized heterogeneous tensor decomposition algorithm for subspace clustering[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071003.

[10] 杨鹏, 刘德儿, 李瑞雪, 等. 结合信息熵与低秩张量分析的金属零件破损检测[J]. 激光与光电子学进展, 2019, 56(21): 211006.

    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, 2019, 56(21): 211006.

[11] 张晓慧, 郝润芳, 李廷鱼. 基于低秩稀疏矩阵分解和稀疏字典表达的高光谱异常目标检测[J]. 激光与光电子学进展, 2019, 56(4): 042801.

    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, 2019, 56(4): 042801.

[12] HassannejadH, MatrellaG, CiampoliniP, 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 Netherlands.New York: ACM Press, 2016: 41- 49.

[13] Wang LM, QiaoY, Tang XO. 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.New York: IEEE Press, 2015: 4305- 4314.

[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, 2015, 10(9): e0137782.

[15] Liu GC, Yan SC. Latent Low-Rank Representation for subspace segmentation and feature extraction[C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain.New York: IEEE Press, 2011: 1615- 1622.

[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, 2018, 30(6): 1081-1094.

[17] Zhang M L, Zhou Z H. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048.

[18] SzegedyC, LiuW, JiaY, et al.Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA.New York: IEEE Press, 2015: 1- 9.

[19] TranD, BourdevL, FergusR, et al.Learning spatiotemporal features with 3D convolutional networks[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile.New York: IEEE Press, 2015: 4489- 4497.

[20] Yeh CK, Wu WC, Ko WJ, et al.Learning deep latent spaces for multi-label classification[C]. 31th AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California. Reston,VA: AIAA Press, 2017: 2838- 2844.

吕卫, 李德盛, 谭浪, 井佩光, 苏育挺. 基于多视角低秩表征的短视频多标签学习模型[J]. 激光与光电子学进展, 2020, 57(22): 221012. 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.

引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!