<title>Texture classification using principle-component analysis techniques</title>

Abstract

We use a traditional principle component analysis approach, i.e. the Karhunen-Loeve Transform (KLT), to evaluate texture features in three feature spaces. The first space is the spatial space with feature vectors formed by raster scan ordering the rows of the texture image into long vectors. The second space is a transformation of the image, such as the DFT. The base of the third feature space is formed by the traditional feature vectors, whose components are the feature values extracted from commonly used algorithms, such as, spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), and the power spectral method (PSM). We apply the algorithms on sidescan sonar image classification and give a performance comparison of the three approaches.