Block linear discriminant analysis for visual tensor objects with frequency or time information

Xutao Li*, Kwok Po NG, Yunming Ye, Eric Ke Wang, Xiaofei Xu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


Recently, due to the advancement of acquisition techniques, visual tensor data have been accumulated in a great variety of engineering fields, e.g., biometrics, neuroscience, surveillance and remote sensing. How to analyze and learn with such tensor objects thus becomes an important and growing interest in machine learning community. In this paper, we propose a block linear discriminant analysis (BLDA) algorithm to extract features for visual tensor objects such as multichannel/hyperspectral face images or human gait videos. Taking the inherent characteristic of such tensor data into account, we unfold tensor objects according to their spatial information and frequency/time information, and represent them in a block matrix form. As a result, the block form between-class and within-class scatter matrices are constructed, and a related block eigen-decomposition is solved to extract features for classification. Comprehensive experiments have been carried out to test the effectiveness of the proposed method, and the results show that BLDA outperforms existing algorithms like DATER, 2DLDA, GTDA, UMLDA, STDA and MPCA for visual tensor object analysis.

Original languageEnglish
Pages (from-to)38-46
Number of pages9
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Nov 2017

Scopus Subject Areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Between-class scatter
  • Discriminant analysis
  • Gait recognition
  • Hyperspectral face recognition
  • Visual tensors
  • Within-class scatter


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