Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 22, pp 29213–29230 | Cite as

Tensor learning and automated rank selection for regression-based video classification

  • Jianguang Zhang
  • Yanbin Liu
  • Jianmin JiangEmail author
Article
  • 270 Downloads

Abstract

The logistic regression is a widely used method for multimedia classification. However, when it is applied to high-order data such as video sequences, traditional vector-based logistic regression often incurs loss of space-time structural information. The tensor extension method based on CP (CANDECOMP/PARAFAC) decomposition is powerful for capturing the multilinear latent information. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank. To effectively exploit underlying space-time structural in video sequences, we propose a tensor-based logistic regression learning algorithm, in which the weight parameter are regarded to be a tensor, calculated after the CP tensor decomposition. We introduce a regularization term, L(2,1)-norm, into the logistic tensor regression, and automatically select the CP rank, making it adaptive to the input videos for improved weight tensor and thus classification performances. Extensive experimental results in comparison with five state-of-the-art regression methods support that our proposed algorithm achieves the best classification performances, providing a good potential for a range of applications towards computerized video classifications via tensor-based video descriptions.

Keywords

Tensor CP (CANDECOMP/PARAFAC) decomposition Video classifications Tensor-based video descriptions 

Notes

Acknowledgments

This work was supported by the Chinese Natural Science Foundation (CNSF) (under Grant 61620106008, Grant 61702165). This work was supported by Shenzhen Commission for Scientific Research & Innovations (under Grant JCYJ20160226191842793). This work was supported by the Hebei Provincial Natural Science Foundation, China (under Grant F2016111005). The work also was supported by the Project of Hebei Province Higher Educational Science and Technology Research (under Grant QN2017513).

References

  1. 1.
    Bootkrajang J, Kabśan A (2014) Learning kernel logistic regression in the presence of class label noise. Pattern Recogn 47(11):3641–3655CrossRefGoogle Scholar
  2. 2.
    Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  3. 3.
    Genkin A, Lewis DD, Madigan D (2007) Large-scale bayesian logistic regression for text categorization. Technometrics 49(3):291–304MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gönen M, Alpaydın E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211– 2268MathSciNetzbMATHGoogle Scholar
  5. 5.
    Guo W, Kotsia I, Patras I (2012) Tensor learning for regression. IEEE Trans Image Process 21(2):816–827MathSciNetCrossRefGoogle Scholar
  6. 6.
    Han Y, Yang Y, Zhou X (2013) Co-regularized ensemble for feature selection. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 1380–1386Google Scholar
  7. 7.
    Han Y, Yang Y, Ma Z, Shen H, Sebe N, Zhou X (2014) Image attribute adaptation. IEEE Trans Multimed 16(4):1115–1126CrossRefGoogle Scholar
  8. 8.
    Han Y, Yang Y, Wu F, Hong R (2015) Compact and discriminative descriptor inference using multi-cues. IEEE Trans Image Process 24(12):5114–5126MathSciNetCrossRefGoogle Scholar
  9. 9.
    Han Y, Yang Y, Yan Y, Ma Z, Sebe N, Zhou X (2015) Semisupervised feature selection via spline regression for video semantic recognition. IEEE Trans Neural Netw Learn Syst 26(2):252–264MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hu H (2013) Enhanced gabor feature based classification using a regularized locally tensor discriminant model for multiview gait recognition. IEEE Trans Circ Syst Video Technol 23(7):1274–1286CrossRefGoogle Scholar
  11. 11.
    Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500MathSciNetCrossRefGoogle Scholar
  12. 12.
    Komarek P (2004) Logistic regression for data mining and high-dimensional classification. Robotics Institute, pp 222Google Scholar
  13. 13.
    Kotsia I, Guo W, Patras I (2012) Higher rank support tensor machines for visual recognition. Pattern Recogn 45(12):4192–4203CrossRefGoogle Scholar
  14. 14.
    Li M, Yuan B (2005) 2d-lda: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26(5):527–532CrossRefGoogle Scholar
  15. 15.
    Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: 2010 IEEE Computer Society conference on computer vision and pattern recognition-workshops. IEEE, pp 9–14Google Scholar
  16. 16.
    Li K, Zhu Y, Yang J, Jiang J (2015) Non-rigid structure from motion via sparse representation. IEEE Trans Cybern 45:1401–1413CrossRefGoogle Scholar
  17. 17.
    Lu H, Plataniotis KN, Venetsanopoulos AN (2008) Mpca: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39CrossRefGoogle Scholar
  18. 18.
    Ma Z, Yang Y, Sebe N, Hauptmann AG (2014) Knowledge adaptation with partiallyshared features for event detectionusing few exemplars. IEEE Trans Pattern Anal Mach Intell 36(9):1789–1802CrossRefGoogle Scholar
  19. 19.
    Pang Y, Li X, Yuan Y (2010) Robust tensor analysis with l1-norm. IEEE Trans Circ Syst Video Technol 20(2):172–178CrossRefGoogle Scholar
  20. 20.
    Sarkar S, Phillips P J, Liu Z, Vega I R, Grother P, Bowyer K W (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177CrossRefGoogle Scholar
  21. 21.
    Tan X, Zhang Y, Tang S, Shao J, Wu F, Zhuang Y (2013) Logistic tensor regression for classification. In: Intelligent science and intelligent data engineering. Springer, Berlin, pp 573–581CrossRefGoogle Scholar
  22. 22.
    Vasilescu MAO, Terzopoulos D (2003) Multilinear subspace analysis of image ensembles. In: 2003 IEEE Computer Society conference on computer vision and pattern recognition, vol 2. IEEE, pp 93–99Google Scholar
  23. 23.
    Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang H-J (2007) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process 16(1):212–220MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yao X, Han J, Zhang D, Nie F (2017) Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans Image Process 26(7):3196–3209MathSciNetCrossRefGoogle Scholar
  25. 25.
    Yu J, Sun J (2017) 3d human pose regression via robust sparse tensor subspace learning. Multimed Tools Appl 76(2):2399–2439CrossRefGoogle Scholar
  26. 26.
    Yun Y, Jiang J (2015) Hybrid sampling-based clustering ensemble with global and local constitutions. IEEE Trans Neural Netw Learn Syst 27(5):952–965MathSciNetGoogle Scholar
  27. 27.
    Zhang J, Han Y, Jiang J (2015) Tucker decomposition-based tensor learning for human action recognition. Multimed Syst 22(3):343–353CrossRefGoogle Scholar
  28. 28.
    Zhang Y, Ren J, Jiang J (2015) Combining mlc and svm classifiers for learning based decision making: analysis and evaluations. Comput Intell Neurosci 2015(2015):8.  https://doi.org/10.1155/2015/423581. Article ID 423581CrossRefGoogle Scholar
  29. 29.
    Zhang D, Meng D, Han J (2017) Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans Pattern Anal Mach Intell 39 (5):865–878CrossRefGoogle Scholar
  30. 30.
    Zhang J, Li Z, Jing P, Liu Y, Su Y (2017) Tensor-driven low-rank discriminant analysis for image set classification. Multimed Tools Appl 1–20Google Scholar
  31. 31.
    Zhu Y, Li K, Jiang J (2014) Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow. Signal Process Image Commun 29(8):875–886CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceHengshui UniversityHengshuiChina
  2. 2.College of Computer Science, Software EngineeringShenzhen UniversityShenzhenChina
  3. 3.Center for Artificial IntelligenceUniversity of Technology SydneySydneyAustralia

Personalised recommendations