Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis

Principal components analysis (PCA) has traditionally been utilized with data expressed in the form of 1-D vectors, but there exists much data such as gray-level images, video sequences, Gabor-filtered images and so on, that are intrinsically in the form of second or higher order tensors. For repres...

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Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 18; číslo 1; s. 36 - 47
Hlavní autoři:	Dong Xu, Shuicheng Yan, Lei Zhang, Lin, S., Hong-Jiang Zhang, Huang, T.S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.01.2008 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Asia Concurrent subspaces analysis (CSA) Detection, estimation, filtering, equalization, prediction dimensionality reduction Exact sciences and technology Face recognition Image analysis Image processing Image reconstruction Image sequence analysis Information, signal and communications theory Mathematical analysis Matrix decomposition Object recognition object reconstruction object representation Pattern recognition Principal component analysis Principal components analysis principal components analysis (PCA) Projection Reconstruction Signal and communications theory Signal processing Signal, noise Studies Subspaces Telecommunications and information theory Tensile stress Tensors Video sequences Object Reconstruction Dimensionality Reduction Concurrent Subspaces Analysis Object Representation Principal Components Analysis Performance evaluation Gabor filter Second order Concurrent subspaces analysis (CSA) Grey level image Image processing Third order Video signal Subspace method Iterative method Pattern recognition Object recognition Algorithm Image reconstruction Statistical method object reconstruction dimensionality reduction Image sequence object representation Object detection Object oriented principal components analysis (PCA) Target detection Principal component analysis
ISSN:	1051-8215, 1558-2205
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Shrnutí:	Principal components analysis (PCA) has traditionally been utilized with data expressed in the form of 1-D vectors, but there exists much data such as gray-level images, video sequences, Gabor-filtered images and so on, that are intrinsically in the form of second or higher order tensors. For representations of image objects in their intrinsic form and order rather than concatenating all the object data into a single vector, we propose in this paper a new optimal object reconstruction criterion with which the information of a high-dimensional tensor is represented as a much lower dimensional tensor computed from projections to multiple concurrent subspaces. In each of these subspaces, correlations with respect to one of the tensor dimensions are reduced, enabling better object reconstruction performance. Concurrent subspaces analysis (CSA) is presented to efficiently learn these subspaces in an iterative manner. In contrast to techniques such as PCA which vectorize tensor data, CSA's direct use of data in tensor form brings an enhanced ability to learn a representative subspace and an increased number of available projection directions. These properties enable CSA to outperform traditional algorithms in the common case of small sample sizes, where CSA can be effective even with only a single sample per class. Extensive experiments on images of faces and digital numbers encoded as second or third order tensors demonstrate that the proposed CSA outperforms PCA-based algorithms in object reconstruction and object recognition.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1051-8215 1558-2205
DOI:	10.1109/TCSVT.2007.903317