NDLI: Concurrent subspaces analysis

Content Provider	IEEE Xplore Digital Library
Author	Dong Xu Shuicheng Yan Lei Zhang Hong-Jiang Zhang Zhengkai Liu Heung-Yeung Shum
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, China (Dong Xu)
Abstract	A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called concurrent subspaces analysis (CSA), to derive representative subspaces by encoding image objects as 2/sup nd/ or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3/sup rd/ order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outperforms principal component analysis in terms of both reconstruction and classification capability.
Sponsorship	IEEE Comput. Soc
Starting Page	203
Ending Page	208
File Size	445096
Page Count	6
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Image sequence analysis Image analysis Algorithm design and analysis Image coding Analytical models Video sequences Digital filters Gabor filters Image databases
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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