Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 193-199.DOI: 10.3778/j.issn.1002-8331.2001-0041

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Transferable Dictionary Learning Fused Data Augmentation

WANG Ziru, LI Zhenmin   

  1. School of Automation, Central South University, Changsha 410083, China
  • Online:2021-12-01 Published:2021-12-02



  1. 中南大学 自动化学院,长沙 410083


A transferable dictionary method is proposed to solve the problem that insufficient label samples in complex behavior dataset. The proposed method uses simple action as the source domain to assist in identifying complex action composed of a series of simple actions. The low-level features of video are extracted by dense trajectory, and then the sparse representation of simple action and complex action are obtained by dictionary learning, and the sparse representation of simple action is used to improve the sparse representation of complex action by transformation matrix. Therefore, even in the case of fewer complex action labeled data, the transferable dictionary can obtain more efficient features. At the same time, GAN is used to data augmentation at the feature level, which helps to learn the dictionary with stronger representation ability. The proposed method is tested on UCF101 and HMDB51 dataset, and obtains better recognition results than the existing method in the case of small sample size, which proves the effectiveness of the method.

Key words: complex action recognition, transferable dictionary, feature augmentation



关键词: 复杂行为识别, 迁移字典, 特征增强