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Robust classwise and projective low-rank representation for image classification

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Abstract

Several variations of the low-rank representation have been suggested intensively for diverse applications, recently. They perform properly on image alignment but undesirably on classification. That is, they are intractable when a new image arrives with an unknown label to be classified. Hence, inspired by a recent research of the fast projection, this paper proposes a supervised approach called the robust classwise and projective low-rank representation (CPLRR), which is the first attempt to align images classwise and learn a projective nonlinear function, simultaneously. It separates out the low-rank components explicitly with the parametric transformation corrections and projects the original images to the low-rank representations of corresponding categories, in an efficient manner. With the advantage of fast projection, CPLRR is appropriate for image classification. Extensive experiments conducted on MNIST, Extended Yale B, and CMU PIE datasets validate the effect of the robust low-rank alignment and the rapid projection, against different domain deformations, noises, and illumination conditions.

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Notes

  1. \(\langle \, X,Y \,\rangle =\mathrm {trace}(X^{\mathrm {T}} Y)\)

  2. \(||\cdot ||_{\mathrm {spec}}\) denotes the spectral norm of matrix.

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Authors and Affiliations

  1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Shengke Xue & Xinyu Jin

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  1. Shengke Xue
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  2. Xinyu Jin
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Correspondence to Xinyu Jin.

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Xue, S., Jin, X. Robust classwise and projective low-rank representation for image classification. SIViP 12, 107–115 (2018). https://doi.org/10.1007/s11760-017-1136-1

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  • DOI: https://doi.org/10.1007/s11760-017-1136-1

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