IEICE TRANSACTIONS on Information
Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Search Algorithm for Vector Quantization
Summary :
A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.3 pp.660-663
- Publication Date
- 2003/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Image Processing, Image Pattern Recognition
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Zhe-Ming LU, Sheng-He SUN, "Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Search Algorithm for Vector Quantization" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 3, pp. 660-663, March 2003, doi: .
Abstract: A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_3_660/_p
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@ARTICLE{e86-d_3_660,
author={Zhe-Ming LU, Sheng-He SUN, },
journal={IEICE TRANSACTIONS on Information},
title={Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Search Algorithm for Vector Quantization},
year={2003},
volume={E86-D},
number={3},
pages={660-663},
abstract={A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Search Algorithm for Vector Quantization
T2 - IEICE TRANSACTIONS on Information
SP - 660
EP - 663
AU - Zhe-Ming LU
AU - Sheng-He SUN
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2003
AB - A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.
ER -
English
