Learning appearance manifolds from video

The appearance of dynamic scenes is often largely governed by a latent low-dimensional dynamic process. We show how to learn a mapping from video frames to this low-dimensional representation by exploiting the temporal coherence between frames and supervision from a user. This function maps the fram...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 868 - 875 vol. 1
Main Authors Rahimi, A., Darrell, T., Recht, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Artificial intelligence
Computer vision
Layout
Lips
Motion analysis
Nonlinear dynamical systems
Nonlinear systems
System identification
Video sequences
Videoconference
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Summary:The appearance of dynamic scenes is often largely governed by a latent low-dimensional dynamic process. We show how to learn a mapping from video frames to this low-dimensional representation by exploiting the temporal coherence between frames and supervision from a user. This function maps the frames of the video to a low-dimensional sequence that evolves according to Markovian dynamics. This ensures that the recovered low-dimensional sequence represents a physically meaningful process. We relate our algorithm to manifold learning, semi-supervised learning, and system identification, and demonstrate it on the tasks of tracking 3D rigid objects, deformable bodies, and articulated bodies. We also show how to use the inverse of this mapping to manipulate video.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.204