Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms
Summary :
An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.6 pp.923-934
- Publication Date
- 2007/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.6.923
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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Kazuya UEKI, Tetsunori KOBAYASHI, "Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 6, pp. 923-934, June 2007, doi: 10.1093/ietisy/e90-d.6.923.
Abstract: An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.6.923/_p
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@ARTICLE{e90-d_6_923,
author={Kazuya UEKI, Tetsunori KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms},
year={2007},
volume={E90-D},
number={6},
pages={923-934},
abstract={An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.},
keywords={},
doi={10.1093/ietisy/e90-d.6.923},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 923
EP - 934
AU - Kazuya UEKI
AU - Tetsunori KOBAYASHI
PY - 2007
DO - 10.1093/ietisy/e90-d.6.923
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2007
AB - An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.
ER -
English
