Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18583
Title: Independent components extraction from image matrix
Authors: Gao, Q
Zhang, L 
Zhang, D 
Xu, H
Keywords: Directional image
Face recognition
Feature extraction
Independent component analysis
Issue Date: 2010
Publisher: North-Holland
Source: Pattern recognition letters, 2010, v. 31, no. 3, p. 171-178 How to cite?
Journal: Pattern recognition letters 
Abstract: The key problem of extracting independent components (ICs) is to learn the demixing matrix from the known training images which can be unfolded to vectors in conventional independent component analysis (ICA). However, the unfolded vectors lead to the small sample size problem (SSS) and the curse of dimensionality. In this paper, a novel independent feature extraction method is proposed to solve these problems by encoding each input image as a matrix. In addition, the row and column directional images of the matrix are introduced to better exploit the spatial and structural information embedded in image during the training phase. Compared with the conventional ICA, the proposed method directly evaluates the two correlated demixing matrices from the image matrix without matrix-to-vector transformation, greatly alleviates the SSS and the curse of dimensionality, reduces the computational complexity, and simultaneously exploits the spatial and structural information embedded in image. Extensive experiments show that the proposed method is superior to the standard ICA method and some unsupervised methods.
URI: http://hdl.handle.net/10397/18583
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2009.10.014
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