| 应用Gabor小波和支持向量机的纹理分类 |
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| 引用本文： | 尚燕,练秋生.应用Gabor小波和支持向量机的纹理分类[J].电视技术,2006(9):14-16,27. |
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| 作者姓名： | 尚燕 练秋生 |
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| 作者单位： | 燕山大学,电子与通信工程系,河北,秦皇岛,066004 |
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| 摘 要： | 针对现有纹理分类算法的局限性,提出了一种基于Gabor小波和支持向量机的纹理分类算法.首先提取纹理Gabor分解后各子带的均值和方差作为特征向量,进而利用支持向量机算法实现分类.实验结果表明,与传统的分类方法相比,Gabor小波和支持向量机相结合能有效地提高分类正确率.
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| 关 键 词： | Gabor小波 支持向量机 特征提取 纹理分类 |
| 文章编号： | 1002-8692（2006）09-0014-03 |
| 收稿时间： | 2006-06-09 |
| 修稿时间： | 2006-06-09 |
Texture Classification Based on Gabor Wavelet and Support Vector Machines |
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| SHANG Yan,LIAN Qiu-sheng.Texture Classification Based on Gabor Wavelet and Support Vector Machines[J].Tv Engineering,2006(9):14-16,27. |
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| Authors: | SHANG Yan LIAN Qiu-sheng |
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| Affiliation: | Dept, of Electronics and Communication, Yanshan University, Hebei Qinhuangdao 066004, China |
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| Abstract: | A texture classification method based on Gabor wavelet and support vector machines(SVM) is proposed while introducing some prevailing classification methods. The feature vector is composed of the mean and standard deviations of the subbands acquired by Gabor decomposition to texture image, then we classify the texture image using SVM algorithm. The experimental results show that the combination of Gabor and SVM can improve the classification rate effectively. |
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| Keywords: | Gabor wavelet support vector machines feature extraction texture classification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
