Supervised Spatio-Temporal Neighborhood Topology Learning for Action Recognition

Supervised manifold learning has been successfully applied to action recognition, in which class label information could improve the recognition performance. However, the learned manifold may not be able to well preserve both the local structure and global constraint of temporal labels in action seq...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2013-08, Vol.23 (8), p.1447-1460
Main Authors: Ma, Andy J., Yuen, Pong C., Zou, Wilman W. W., Jian-Huang Lai
Format: Article
Language:English
Subjects:
Action recognition
Applied sciences
Context
Detection, estimation, filtering, equalization, prediction
Eigenvalues and eigenfunctions
Exact sciences and technology
Feature extraction
Image processing
Information, signal and communications theory
manifold learning
Manifolds
neighborhood topology learning
Pattern recognition
Signal and communications theory
Signal processing
Signal, noise
supervised spatial
Telecommunications and information theory
temporal pose correspondence
Topology
Vectors
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Summary:Supervised manifold learning has been successfully applied to action recognition, in which class label information could improve the recognition performance. However, the learned manifold may not be able to well preserve both the local structure and global constraint of temporal labels in action sequences. To overcome this problem, this paper proposes a new supervised manifold learning algorithm called supervised spatio-temporal neighborhood topology learning (SSTNTL) for action recognition. By analyzing the topological characteristics in the context of action recognition, we propose to construct the neighborhood topology using both supervised spatial and temporal pose correspondence information. Employing the property in locality preserving projection (LPP), SSTNTL solves the generalized eigenvalue problem to obtain the best projections that not only separates data points from different classes, but also preserves local structures and temporal pose correspondence of sequences from the same class. Experimental results demonstrate that SSTNTL outperforms the manifold embedding methods with other topologies or local discriminant information. Moreover, compared with state-of-the-art action recognition algorithms, SSTNTL gives convincing performance for both human and gesture action recognition.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2013.2248494