| 采用精选Gabor小波和SVM分类的物体识别 |
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| 引用本文： | 沈琳琳,纪震.采用精选Gabor小波和SVM分类的物体识别[J].自动化学报,2009,35(4):350-355. |
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| 作者姓名： | 沈琳琳 纪震 |
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| 作者单位： | 1.深圳大学计算机与软件学院 深圳 518060 |
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| 基金项目： | 国家自然科学基金，Royal Society (U.K.) International Joint Projects 2006/R3-Cost Share with NSFC，ScienceFoundation of Shenzhen City，教育部留学回国人员科研启动基金 |
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| 摘 要： | 提出了一种基于Gabor小波和支持向量机的物体识别通用框架. 在该框架中, 特征抽取采用选取的Gabor小波在物体的最佳位置卷积实现, 而分类则通过支持向量机实现. 相比传统的基于Gabor特征的识别系统, 该方法能够同时达到准确而快速的分类目的. 本论文成功地将该框架应用于两个实际的物体识别例子: 物体/非物体分类和人脸识别. 实验结果证明了所提出的方法相对于其它方法的优越性.
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| 关 键 词： | Gabor特征 支持向量机 物体识别 |
| 收稿时间： | 2008-1-28 |
| 修稿时间： | 2008-4-28 |
Gabor Wavelet Selection and SVM Classification for Object Recognition |
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| SHEN Lin-Lin JI Zhen .School of Computer , Software Engineering,Shenzhen University,Shenzhen ,P.R.China.Gabor Wavelet Selection and SVM Classification for Object Recognition[J].Acta Automatica Sinica,2009,35(4):350-355. |
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| Authors: | SHEN Lin-Lin JI Zhen School of Computer Software Engineering Shenzhen University Shenzhen PRChina |
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| Affiliation: | 1.School of Computer and Software Engineering, Shenzhen University, Shenzhen 518060, P.R. China |
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| Abstract: | This paper proposes a Gabor wavelets and support vector machine (SVM)-based framework for object recognition. When discriminative features are extracted at optimized locations using selected Gabor wavelets, classifications are done via SVM. Compared to conventional Gabor feature based object recognition system, the system developed in this paper is both robust and efficient. The proposed framework has been successfully applied to two object recognition applications, i.e., object/non-object classification and face recognition. Experimental results clearly show advantages of the proposed method over other approaches. |
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| Keywords: | Gabor feature support vector machine (SVM) object recognition |
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