抄録
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 twodimensional 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.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 923-934 |
| ページ数 | 12 |
| ジャーナル | IEICE Transactions on Information and Systems |
| 巻 | E90-D |
| 発行部数 | 6 |
| DOI | |
| 出版物ステータス | Published - 2007 6 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Software
- Artificial Intelligence
- Hardware and Architecture
- Computer Vision and Pattern Recognition
これを引用
Fusion-based age-group classification method using multiple two-dimensional feature extraction algorithms. / Ueki, Kazuya; Kobayashi, Tetsunori.
：: IEICE Transactions on Information and Systems, 巻 E90-D, 番号 6, 06.2007, p. 923-934.研究成果: Article
}
TY - JOUR
T1 - Fusion-based age-group classification method using multiple two-dimensional feature extraction algorithms
AU - Ueki, Kazuya
AU - Kobayashi, Tetsunori
PY - 2007/6
Y1 - 2007/6
N2 - 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 twodimensional 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.
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 twodimensional 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.
KW - 2DLDA
KW - 2DPCA
KW - Age-group classification
KW - Classification combination
KW - Face recognition pattern recognition
KW - Max rule
KW - Min rule
KW - Min-max normalization
KW - Produc rule
KW - Sum rule
KW - Z-score
UR - http://www.scopus.com/inward/record.url?scp=50249152185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50249152185&partnerID=8YFLogxK
U2 - 10.1093/ietisy/e90-d.6.923
DO - 10.1093/ietisy/e90-d.6.923
M3 - Article
AN - SCOPUS:50249152185
VL - E90-D
SP - 923
EP - 934
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 6
ER -