Linear Combination of Texture Features Based on Partial Least Square Regression

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.