IEICE TRANSACTIONS on Information
Independent Component Analysis for Color Indexing
Summary :
Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.4 pp.997-1003
- Publication Date
- 2004/04/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
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Xiang-Yan ZENG, Yen-Wei CHEN, Zensho NAKAO, Jian CHENG, Hanqing LU, "Independent Component Analysis for Color Indexing" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 4, pp. 997-1003, April 2004, doi: .
Abstract: Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_4_997/_p
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@ARTICLE{e87-d_4_997,
author={Xiang-Yan ZENG, Yen-Wei CHEN, Zensho NAKAO, Jian CHENG, Hanqing LU, },
journal={IEICE TRANSACTIONS on Information},
title={Independent Component Analysis for Color Indexing},
year={2004},
volume={E87-D},
number={4},
pages={997-1003},
abstract={Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Independent Component Analysis for Color Indexing
T2 - IEICE TRANSACTIONS on Information
SP - 997
EP - 1003
AU - Xiang-Yan ZENG
AU - Yen-Wei CHEN
AU - Zensho NAKAO
AU - Jian CHENG
AU - Hanqing LU
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2004
AB - Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.
ER -
English
