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基于Fisher的Gabor特征提取方法
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2009-06-06
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提出在Gabor 滤波理论的基础上，结合Fisher 线性判别方法，对手写数字图像的所
有特征点寻找局部最优滤波频率和滤波方向，从而提取最优Gabor 特征的方法。对MNIST 手写体数据库的识别实验表明，该方法在小样本情况下明显优于直接Gabor 特征提取。
关键词：Gabor 滤波；Fisher 线性判别；手写数字识别；特征提取；LIBSVM
Extract Gabor Features Based on Fisher’s Discriminant ZHOU hui-can1, LIU qiong1，WANG yao-nan2 (1.Hunan University of Art and Science, Department of Computer Science, Changde Hunan 415000，China； 2.Hunan University, College of Electrical and Information Engineering, Changsha Hunan 410082，China) Abstract：A method for extracting optimal Gabor features is presented in this paper, witch bases on the theory of Gabor filters and combines the Fisher’s linear discriminant. In an image of handwritten numeral, the above-mentioned method tries to find the local optimal frequencies and orientations for filtering at all of the feature points. An experiment of handwritten numeral recognition to MNIST database indicates that the method has an advantage of directly extracting Gabor features conditions of small sample obviously.
Key words：Gabor filters；Fisher’s linear discriminant；Handwritten numeral recognition；Features extracting；LIBSVM
有特征点寻找局部最优滤波频率和滤波方向，从而提取最优Gabor 特征的方法。对MNIST 手写体数据库的识别实验表明，该方法在小样本情况下明显优于直接Gabor 特征提取。
关键词：Gabor 滤波；Fisher 线性判别；手写数字识别；特征提取；LIBSVM
Extract Gabor Features Based on Fisher’s Discriminant ZHOU hui-can1, LIU qiong1，WANG yao-nan2 (1.Hunan University of Art and Science, Department of Computer Science, Changde Hunan 415000，China； 2.Hunan University, College of Electrical and Information Engineering, Changsha Hunan 410082，China) Abstract：A method for extracting optimal Gabor features is presented in this paper, witch bases on the theory of Gabor filters and combines the Fisher’s linear discriminant. In an image of handwritten numeral, the above-mentioned method tries to find the local optimal frequencies and orientations for filtering at all of the feature points. An experiment of handwritten numeral recognition to MNIST database indicates that the method has an advantage of directly extracting Gabor features conditions of small sample obviously.
Key words：Gabor filters；Fisher’s linear discriminant；Handwritten numeral recognition；Features extracting；LIBSVM
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