DSVD: A tensor-based image compression and recognition method

Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalConference articlepeer-review

    17 Citations (Scopus)


    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 languageEnglish
    Article number1466083
    Pages (from-to)6308-6311
    Number of pages4
    JournalProceedings - IEEE International Symposium on Circuits and Systems
    Publication statusPublished - 2005
    EventIEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan
    Duration: May 23 2005May 26 2005

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering


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