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


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.


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



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).


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© 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

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