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Discriminative Non-Linear Stationary Subspace Analysis for Video Classification | IEEE Journals & Magazine | IEEE Xplore

Discriminative Non-Linear Stationary Subspace Analysis for Video Classification


Abstract:

Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to acc...Show More

Abstract:

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.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 36, Issue: 12, 01 December 2014)
Page(s): 2353 - 2366
Date of Publication: 16 July 2014

ISSN Information:

PubMed ID: 26353144

Funding Agency:


References

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