Learning compact representation for image with tensor manifold perspective

Authors:
Songsong Wu;Zhisen Wei;Xiaoyuan Jing;Jian Yang;Jingyu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China,School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, P.R. ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China;School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, P.R. China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

In this paper, Local Tensor Subspace Alignment algorithm (LTESA) is proposed to explore the substantial geometry of image manifold by regarding images as tensor objects. LTESA characterizes local geometry of tensor in local tensor subspace with rank-one tensor approximation, then align the local tensor subspaces to achieve a global low-dimensional representation for images. LTESA obtains the intrinsic latent variables of image through nonlinear dimensionality and achieves compactness of image representation in vector form. Moreover, Landmark-LTESA is proposed to reduce computational complexity of LTESA and a generalization version of LTESA is proposed to solve the out-of-sample problem for image feature extraction. LTESA is evaluated in applications of data visualization for face images and face recognition. Experimental results suggest that the proposed approaches provide a strong capability of detecting complex image manifold and is effective on unsupervised nonlinear feature extraction of image.