Towards a texture naming system: identifying relevant dimensions of texture.

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 Brodatz's album on 12 nine-point Likert scales. Each subject was also asked to group these pictures into as many classes as desired. We applied the techniques of hierarchical cluster analysis and non-parametric multidimensional scaling (MDS) to the pooled similarity matrix generated from the subjects' groupings. We used Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis on the data from the scale ratings. The clusters generated from hierarchical cluster analysis remained intact in the MDS plots. We found that the MDS solutions fit the data well. The stress in the three-dimensional case is 0.12. The CART and discriminant analyses provided further justification for our interpretation. The three orthogonal dimensions we identified for texture are 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.