  Tactile sensing or fabric hand plays a critical role in an individual's
decision to buy a certain fabric from the range of available fabrics for a
desired application. Therefore, textile and clothing manufacturers have long
been in search of an objective method for assessing fabric hand, which can then
be used to engineer fabrics with a desired hand. Recognizing textures and
materials in real-world images has played an important role in object
recognition and scene understanding. In this paper, we explore how to
computationally characterize apparent or latent properties (e.g., surface
smoothness) of materials, i.e., computational material surface
characterization, which moves a step further beyond material recognition. We
formulate the problem as a very fine-grained texture classification problem,
and study how deep learning-based texture representation techniques can help
tackle the task. We introduce a new, large-scale challenging microscopic
material surface dataset (CoMMonS), geared towards an automated fabric quality
assessment mechanism in an intelligent manufacturing system. We then conduct a
comprehensive evaluation of state-of-the-art deep learning-based methods for
texture classification using CoMMonS. Additionally, we propose a multi-level
texture encoding and representation network (MuLTER), which simultaneously
leverages low- and high-level features to maintain both texture details and
spatial information in the texture representation. Our results show that, in
comparison with the state-of-the-art deep texture descriptors, MuLTER yields
higher accuracy not only on our CoMMonS dataset for material characterization,
but also on established datasets such as MINC-2500 and GTOS-mobile for material
recognition.
