Title :
DSVD: a tensor-based image compression and recognition method
Author :
Inoue, Kohei ; Urahama, Kiichi
Author_Institution :
Dept. of Visual Commun. Design, Kyushu Univ., Fukuoka, Japan
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.
Keywords :
data compression; face recognition; image coding; matrix algebra; optimisation; principal component analysis; singular value decomposition; tensors; DSVD; data matrices; dyadic singular value decomposition; face recognition; higher-order singular value decomposition; image recognition; low-rank approximation; optimal dimensionality reduction; principal component analysis; tensor-based image compression; third-order tensor; truncated singular value decomposition; Discrete wavelet transforms; Face recognition; Image coding; Image recognition; Iterative algorithms; Iterative methods; Matrix decomposition; Principal component analysis; Singular value decomposition; Tensile stress;
Conference_Titel :
Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
Print_ISBN :
0-7803-8834-8
DOI :
10.1109/ISCAS.2005.1466083