Texture, along with color, is one of the most important characteristics of a material defining its appearance. While color had been studied for a long time and continues being an interesting topic, the analysis of texture has traditionally been postponed, mainly because of its difficulty, and remains a challenge. Depending on the application, different approaches to texture characterization have been proposed in the bibliography. In this work, texture is considered in the context of visual perception and the second order statistical measurements based on the Grey-Level Co-occurrence Matrix (GLCM) have been computed for a database of texture images (KTH-TIPS and KTH-TIPS2). In the literature, there is no available information about the number of features needed for texture characterization, although no less than five parameters are typically employed. In our previous work, the selection of the optimal texture features was studied through Principal Component Analysis (PCA), using only those that are statistically significant describing the studied textures. In this work, the texture features obtained were analyzed from a perceptual point of view.
Ana Gebejes, Rafael Huertas, Alain Tremeau, Ivana Tomic, Pooshpanjan R. Biswas, Charlotte Fraza, Markku Hauta-Kasari, "Texture Characterization by Grey-Level Co-occurrence Matrix from a Perceptual Approach" in Proc. IS&T 24th Color and Imaging Conf. , 2016, https://doi.org/10.2352/ISSN.2169-2629.2017.32.271