Towards a texture naming system: Identifying relevant dimensions of texture

Abstract

Recently, researchers have started using texture for data visualization. The rationale behind this is to exploit the sensitivity of the human visual system to texture in order to overcome the limitations inherent in the display of multidimensional data. A fundamental issue that must be addressed is what textural features are important in texture perception, and how they are used. We designed an experiment to help identify the relevant higher order features of texture perceived by humans. We used twenty subjects, who were asked to rate 56 pictures from Bro-datz's album [1] on 12 nine-point Likert scales. We applied the techniques of hierarchical cluster analysis, non-parametric multidimensional scaling (MDS), Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis to data gathered from the subjects. Based on these techniques, we identified three orthogonal dimensions for texture to be repetitive vs. non-repetitive; high-contrast and non-directional vs. low-contrast and directional; granular, coarse and low-complexity vs. non-granular, fine and high-complexity.