M-HinTS: mimicking humans in texture sorting

Various texture analysis algorithms have been developed the last decades. However, no computational model has arisen that mimics human texture perception adequately. In 2000, Payne, Hepplewhite, and Stoneham and in 2005, Van Rikxoort, Van den Broek, and Schouten achieved mappings between humans and artificial classifiers of respectively around 29% and 50%. In the current research, the work of Van Rikxoort et al. was replicated, using the newly developed, online card sorting experimentation platform M-HinTS: http://eidetic.ai.ru. nl/M-HinTS/. In two separate experiments, color and gray scale versions of 180 textures, drawn from the OuTex and VisTex texture databases were clustered by 34 subjects. The mutual agreement among these subjects was 51% and 52% for, respectively, the experiments with color and gray scale textures. The average agreement between the k-means algorithm and the participants was 36%, where k-means approximated some participants up to 60%. Since last year’s results were not replicated, an additional data analysis was developed, which uses the semantic labels available in the database. This analysis shows that semantics play an important role in human texture clustering and once more illustrate the complexity of texture recognition. The current findings, the introduction of M-HinTS, and the set of analyzes discussed, are the start of a next phase in unraveling human texture recognition.