DSVD: A tensor-based image compression and recognition method

Kohei Inoue; Kiichi Urahama

doi:10.1109/ISCAS.2005.1466083

DSVD: A tensor-based image compression and recognition method

Kohei Inoue, Kiichi Urahama

Research output: Contribution to journal › Conference article › peer-review

17 Citations (Scopus)

Abstract

Optimal dimensionality reduction of a single matrix is given by the truncated singular value decomposition, and optimal compression of a set of vector data is given by the principal component analysis. We present, in this paper, a dyadic singular value decomposition (DSVD) which gives a near-optimal dimensionality reduction of a set of matrix data and apply it to image compression and face recognition. The DSVD algorithm is derived from the higher-order singular value decomposition (HOSVD) of a third-order tensor and gives an analytical solution of a low-rank approximation problem for data matrices. The DSVD outperforms the other dimensionality reduction methods in the computational speed and accuracy in image compression. Its face recognition rate is higher than the eigenface method.

Original language	English
Article number	1466083
Pages (from-to)	6308-6311
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/ISCAS.2005.1466083
Publication status	Published - 2005
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: May 23 2005 → May 26 2005

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/ISCAS.2005.1466083

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title = "DSVD: A tensor-based image compression and recognition method",

abstract = "Optimal dimensionality reduction of a single matrix is given by the truncated singular value decomposition, and optimal compression of a set of vector data is given by the principal component analysis. We present, in this paper, a dyadic singular value decomposition (DSVD) which gives a near-optimal dimensionality reduction of a set of matrix data and apply it to image compression and face recognition. The DSVD algorithm is derived from the higher-order singular value decomposition (HOSVD) of a third-order tensor and gives an analytical solution of a low-rank approximation problem for data matrices. The DSVD outperforms the other dimensionality reduction methods in the computational speed and accuracy in image compression. Its face recognition rate is higher than the eigenface method.",

author = "Kohei Inoue and Kiichi Urahama",

year = "2005",

doi = "10.1109/ISCAS.2005.1466083",

language = "English",

pages = "6308--6311",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

}

TY - JOUR

T1 - DSVD

T2 - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

AU - Inoue, Kohei

AU - Urahama, Kiichi

PY - 2005

Y1 - 2005

N2 - Optimal dimensionality reduction of a single matrix is given by the truncated singular value decomposition, and optimal compression of a set of vector data is given by the principal component analysis. We present, in this paper, a dyadic singular value decomposition (DSVD) which gives a near-optimal dimensionality reduction of a set of matrix data and apply it to image compression and face recognition. The DSVD algorithm is derived from the higher-order singular value decomposition (HOSVD) of a third-order tensor and gives an analytical solution of a low-rank approximation problem for data matrices. The DSVD outperforms the other dimensionality reduction methods in the computational speed and accuracy in image compression. Its face recognition rate is higher than the eigenface method.

AB - Optimal dimensionality reduction of a single matrix is given by the truncated singular value decomposition, and optimal compression of a set of vector data is given by the principal component analysis. We present, in this paper, a dyadic singular value decomposition (DSVD) which gives a near-optimal dimensionality reduction of a set of matrix data and apply it to image compression and face recognition. The DSVD algorithm is derived from the higher-order singular value decomposition (HOSVD) of a third-order tensor and gives an analytical solution of a low-rank approximation problem for data matrices. The DSVD outperforms the other dimensionality reduction methods in the computational speed and accuracy in image compression. Its face recognition rate is higher than the eigenface method.

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DO - 10.1109/ISCAS.2005.1466083

M3 - Conference article

AN - SCOPUS:33845562150

SN - 0271-4310

SP - 6308

EP - 6311

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

M1 - 1466083

Y2 - 23 May 2005 through 26 May 2005

ER -

DSVD: A tensor-based image compression and recognition method

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