Selection of optimal features for texture characterization and perception
Ana Gebejes, Rafael Huertas, Ivana Tomić, M. Stepanic · 2013
Different approach to texture characterization can be considered. In this work texture are analyzed through second order statistical measurements based on the Grey-Level Co-occurrence Matrix proposed by Haralick [1]. By this method is possible to compute 22 different features to describe texture. Usually, in previous works, only 5 features are considered among the complete set, but no reasons are exposed for that selection. In this work, using Principal Component Analysis, the set of features is studied and 5 features, different from former, are proposed as the most convenient describing and characterizing the considered textures. Finally, the relationship between the proposed features and perception of texture is analyzed.