| 改进的统计不相关最优鉴别矢量集 |
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| 引用本文： | 吴小俊, 杨静宇, 王士同, JosefKittler, 陆介平. 改进的统计不相关最优鉴别矢量集[J]. 电子与信息学报, 2005, 27(1): 47-50. |
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| 作者姓名： | 吴小俊 杨静宇 王士同 Josef Kittler 陆介平 |
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| 作者单位： | 江苏科技大学计算机系,镇江,212003;中国科学院沈阳自动化所机器人学重点实验室,沈阳,110015;CVSSP,Dept.of Electrical Engineering,University of Surrey GU2 7XH,UK;南京理工大学信息学院,南京,210094;CVSSP,Dept.of Electrical Engineering,University of Surrey GU2 7XH,UK;江苏科技大学计算机系,镇江,212003 |
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| 基金项目： | 国家自然科学基金;中国科学院重点实验室基金;江苏省高校自然科学基金;江苏省自然科学基金;图像处理与图像通信实验室基金 |
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| 摘 要： | 该文对统计不相关最优鉴别矢量集算法进行研究,在分析统计不相关最优鉴别矢量集算法的基础上提出了一种改进的方法。该方法在类内散布矩阵的特征空间中求解统计不相关最优鉴别矢量集。为了加快特征抽取速度,利用基于图像鉴别分析的维数压缩方法,对图像数据进行了压缩。在ORL和Yale人脸数据库的数值实验,验证本文所提出的方法的有效性。
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| 关 键 词： | 模式识别 特征抽取 鉴别分析 最佳鉴别矢量集 人脸识别 |
| 文章编号： | 1009-5896(2005)01-0047-04 |
| 收稿时间： | 2003-05-28 |
| 修稿时间： | 2003-05-28 |
An Improved Optimal Set of Statistical Uncorrelated Discriminant Vectors |
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| Wu Xiao-Jun, Yang Jing-Yu, Wang Shi-Tong, Josef Kittler, Lu Jie-ping. An Improved Optimal Set of Statistical Uncorrelated Discriminant Vectors[J]. Journal of Electronics & Information Technology, 2005, 27(1): 47-50. |
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| Authors: | Wu Xiao-Jun Yang Jing-Yu Wang Shi-Tong Josef Kittler Lu Jie-ping |
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| Affiliation: | Dept of Computer Science Jiangsu Univ. of Sci. and Tech.,Zhenjiang 212003 China;Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110015 China;CVSSP Dept of Electrical Engineering University of Surrey GU27XH,UK;School of Information Nanjing University of Science & Technology Nanjing 210094 China |
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| Abstract: | This paper presents a research on the algorithm of optimal set of statistically uncorrelated discriminant vectors. An improved algorithm has been proposed on the basis of the analysis of the conventional algorithm of statistical uncorrelated discriminant vectors, which solves the optimal set of statistically uncorrelated discriminant vectors in the eigen space of the within-class scatter matrix Sw . The dimension of images has been reduced using the dimension reduction method based on image discriminant analysis in order to speed the process of feature extraction. The numerical experiments on facial databases of ORL and Yale show the effectiveness of the proposed method. |
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| Keywords: | Pattern recognition Feature extraction Discriminant analysis Optimal set of discriminant vectors Face recognition |
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