Carpet manufacturers certify their products with labels corresponding to the capability of the carpets in retaining the original appearance. Traditionally, these labels are subjectively defined by reference cases where human experts evaluate the degree of wear, which is quantified by a number called the wear label. Industry is very interested in converting these traditional standards to automated objective standards. With this purpose, research has been conducted using image analysis with either depth or intensity data. In this paper, we present a comparison of texture features extracted from both types of images. For this, we scanned 3D data and photographed eight types of images provided from the EN1471 standard. The features are extracted comparing the distribution of Local Binary Patterns (LBPs) computed from images of original and change in appearance. We assess how well we can arrange the features in the order of the wear labels and count the number of consecutive wear labels that can be statistically distinguished. We found that two of the eight carpet types are properly described using depth data and five using intensity data while one type could not be described. These results suggest that both types of images can be complementary used for representing the wear labels. This can lead to an automated and universal labeling system for carpets.