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A Tensor-Driven Temporal Correlation Model for Video Sequence Classification
- Source :
- IEEE Signal Processing Letters. 23:1246-1249
- Publication Year :
- 2016
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2016.
-
Abstract
- The task of video sequence classification plays a critical role in the development of computer vision. Considering this fact, this letter proposes a novel tensor decomposition method called tensor-driven temporal correlation in which general tensors are used as input for video sequence classification. Because distortion and redundancy may exist in the tensor representations of video sequences, we project the original tensor into subspaces spanned by spatial basis matrices in the proposed formulation. Moreover, to better preserve the temporal smoothness between consecutive slices of the tensor, the basis matrices are jointly learned by introducing an autoregressive model. An experiment on the commonly used Cambridge hand-gesture database demonstrates that our proposed method reaches convergence within a small number of iterations during the training stage and achieves promising results compared with state-of-the-art methods.
- Subjects :
- Basis (linear algebra)
business.industry
Applied Mathematics
Feature extraction
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Linear subspace
Autoregressive model
Distortion
Signal Processing
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
Redundancy (engineering)
020201 artificial intelligence & image processing
Tensor
Artificial intelligence
Electrical and Electronic Engineering
business
Mathematics
Subjects
Details
- ISSN :
- 15582361 and 10709908
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- IEEE Signal Processing Letters
- Accession number :
- edsair.doi...........e0afca032e66dd9467eab6f0fbdd5bf3