Discriminative Non-Linear Stationary Subspace Analysis for Video Classification

Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting repre...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2014-12, Vol.36 (12), p.2353-2366
Main Authors: Baktashmotlagh, Mahsa, Harandi, Mehrtash, Lovell, Brian C., Salzmann, Mathieu
Format: Article
Language:English
Subjects:
Accounting
Algorithm design and analysis
Classification
Eigenvalues and eigenfunctions
Image classification
Image reconstruction
Linear programming
Nonlinearity
Pattern analysis
Principal component analysis
Representations
Subspaces
Texture recognition
Video signals
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Summary:Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce non-linear stationary subspace analysis: a method that overcomes this issue by explicitly separating the stationary parts of the video signal (i.e., the parts shared across all videos in one class), from its non-stationary parts (i.e., the parts specific to individual videos). Our method also encourages the new representation to be discriminative, thus accounting for the underlying classification problem. We demonstrate the effectiveness of our approach on dynamic texture recognition, scene classification and action recognition.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2014.2339851