Does Attribute Space Driven Tactile Features Correspond to Overall Perceptual Similarity of Textures?

In the recent past, various hand-crafted tactile features have been extracted from object surfaces for different perceptual attributes such as hardness, roughness, and friction, and they are used to define the physical space. In this paper, we question whether these state-of-the-art tactile features corresponding to hardness, roughness, and friction explain the overall dissimilarity/similarity rating between different texture pairs or not. Our approach follows two steps. Firstly, we investigate whether the regressor-based distance learned from these handcrafted features corresponds to perceptual space. Different regressors are learned on the fixed set of features. The results show that the regressor-based learning of distance between texture pairs attains a moderate level of agreement (in terms of mean square error, Spearman correlation, and Pearson correlation) with perceptual dissimilarities of corresponding pairs (i.e., perceptual space). Different regressors have provided almost similar accuracy. Second, we determine the relevance of a structure in tactile features in finding mapping with the perceptual space with the help of the permutation-test-based approach, where the features are perturbed. We hypothesize that if there is any structure in physical space, then any perturbation to it will provide a bad mapping function between the physical and perceptual spaces. The permutation test results reject the hypothesis, indicating that the employed tactile features (corresponding to hardness, roughness, and friction) are not optimal to justify the overall perceptual dissimilarity between texture pairs. The results of the study may be employed in different applications of haptics in the area of human-computer interaction, tele-robotics, augmented and virtual reality, and material classification.