Identifying High Level Features of Texture Perception

Abstract

A fundamental issue in texture analysis is that of deciding what textural features are important in texture perception, and how they are used. Experiments on human preattentive vision have identified several low-level features (such as orientation of blobs and size of line segments), which are used in texture perception. However, the question of what higher level features of texture are used has not been adequately addressed. We designed an experiment to help identify the relevant higher order features of texture perceived by humans. We used 20 subjects, who were asked to perform an unsupervised classification of 30 pictures from Brodatz′s album on texture. Each subject was asked to group these pictures into as many classes as desired. Both hierarchical cluster analysis and nonparametric multidimensional scaling (MDS) were applied to the pooled similarity matrix generated from the subjects′ groupings. A surprising outcome is that the MDS solutions fit the data very well. The stress in the two-dimensional case is 0.10, and the stress in the three-dimensional case is 0.045. We rendered the original textures in these coordinate systems, and interpreted the (rotated) axes. It appears that the axes in the 2D case correspond to periodicity versus irregularity, and directionality versus nondirectionality. In the 3D case, the third dimension represents the structural complexity of the texture. Furthermore, the clusters identified by the hierarchical cluster analysis remain virtually intact in the MDS solution. The results of our experiment indicate that people use three high-level features for texture perception. Future studies are needed to determine the appropriateness of these high-level features for computational texture analysis and classification.

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