| 基于偏最小二乘回归的纹理特征线性组合 |
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| 引用本文： | 金淑英,李德仁,龚健雅.基于偏最小二乘回归的纹理特征线性组合[J].武汉大学学报(信息科学版),2006,31(5):399-402. |
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| 作者姓名： | 金淑英 李德仁 龚健雅 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室,武汉市珞喻路129号,430079 |
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| 摘 要： | 基于偏最小二乘回归技术时纹理特征进行线性组合，得到新的纹理特征来进行分类。实验表明，组合后的纹理特征不但提高了纹理分类的性能，而且具有一定的数据自适应能力。
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| 关 键 词： | 特征组合 特征选择 共生矩阵 纹理谱 偏最小二乘回归 |
| 文章编号： | 1671-8860(2006)05-0399-04 |
| 修稿时间： | 2006年2月18日 |
Linear Combination of Texture Features Based on Partial Least Square Regression |
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| JIN Shuying,LI Deren,GONG Jianya.Linear Combination of Texture Features Based on Partial Least Square Regression[J].Geomatics and Information Science of Wuhan University,2006,31(5):399-402. |
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| Authors: | JIN Shuying LI Deren GONG Jianya |
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| Abstract: | The paper presents partial least squares(PLS) method.Firstly,texture features(spectrum(TS) and gray-level co-occurrence matrix(GLCM)) are calculated from local image regions.Secondly,the authors apply PLS regression to preparatory texture features to extract linear combined new texture features.Thirdly,both the linear combined texture features and the preparatory texture features,together with the ordinary texture features,are imported into linear discrimination analysis(LDA) and quadratic discrimination analysis(QDA).Finally,classification results are compared and conclusions are drawn.The experiments show that not only PLS can reduce the dimension of texture features but also the combined texture features efficiently have better discrimination abilities than the ordinary texture features. |
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| Keywords: | feature combination feature selection GLCM texture spectrum partial least square regression |
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