Perceptual Similarity: A Texture Challenge

Texture classification and segmentation have been extensively researched over the last thirty years. Early on the Brodatz album[1] quickly became the de facto standard in which a texture class comprised a set of nonoverlapping sub-images cropped from a single photograph. Later, as the focus shifted to investigating illuminationand pose-invariant algorithms, the CUReT database[3] became popular and the texture class became the set of photographs of a single physical sample captured under a variety of imaging conditions. While extremely successful algorithms have been developed to address classification problems based on these databases, the challenging problem of measuring perceived inter-class texture similarity has rarely been discussed. This paper makes use of a new texture collection[4]. It comprises 334 texture samples, including examples of embossed vinyl, woven wall coverings, carpets, rugs, window blinds, soft fabrics, building materials, product packaging, etc. Additionally, an associated perceptual similarity matrix is provided. This was obtained from a grouping experiment using 30 observers. The similarity scores, S(Ii, I j), for each texture pair were calculated simply by dividing the number of observers that grouped the pair into the same sub-set by the number of observers that had the opportunity do so. A dissimilarity matrix was then defined as dsim(Ii, I j) = 1− S(Ii, I j). Hence dsim(Ii, Ii) = 0 for all images Ii, and dsim(Ii, I j) = 1 if none of the participants grouped images Ii together with I j.