基于偏最小二乘回归的纹理特征线性组合

详细信息

Linear Combination of Texture Features Based on Partial Least Square Regression

¹ State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,129 Luoyu Road,Wuhan 430079,China

摘要: 基于偏最小二乘回归技术对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。
- 特征组合 /
- 特征选择 /
- 共生矩阵 /
- 纹理谱 /
- 偏最小二乘回归
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.
- feature combination /
- feature selection /
- GLCM /
- texture spectrum /
- partial least square regression