NDLI: Unsupervised Random Forest Manifold Alignment for Lipreading

Content Provider	IEEE Xplore Digital Library
Author	Yuru Pei Tae-Kyun Kim Hongbin Zha
Copyright Year	2013
Description	Author affiliation: Imperial Coll. London, London, UK (Tae-Kyun Kim) \|\| Peking Univ., Beijing, China (Yuru Pei; Hongbin Zha)
Abstract	Lip reading from visual channels remains a challenging topic considering the various speaking characteristics. In this paper, we address an efficient lip reading approach by investigating the unsupervised random forest manifold alignment (RFMA). The density random forest is employed to estimate affinity of patch trajectories in speaking facial videos. We propose novel criteria for node splitting to avoid the rank-deficiency in learning density forests. By virtue of the hierarchical structure of random forests, the trajectory affinities are measured efficiently, which are used to find embeddings of the speaking video clips by a graph-based algorithm. Lip reading is formulated as matching between manifolds of query and reference video clips. We employ the manifold alignment technique for matching, where the $L_{∞}-norm-based$ manifold-to-manifold distance is proposed to find the matching pairs. We apply this random forest manifold alignment technique to various video data sets captured by consumer cameras. The experiments demonstrate that lip reading can be performed effectively, and outperform state-of-the-arts.
Sponsorship	IEEE Comput. Soc.
Starting Page	129
Ending Page	136
File Size	729610
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Videos Trajectory Vegetation Image color analysis Covariance matrices Shape RFMA Unsupervised Random Forest Manifold Alignment Lipreading
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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