Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset

TL;DR

This paper introduces a new challenging dataset for fabric surface characterization and evaluates deep learning methods, proposing MuLTER, a multi-level texture encoding network that improves accuracy in material classification tasks.

Contribution

The paper presents a new large-scale dataset for fabric surface analysis and introduces MuLTER, a novel deep learning architecture that enhances texture representation for material classification.

Findings

MuLTER outperforms existing deep texture descriptors on CoMMonS, MINC-2500, and GTOS-mobile datasets.

The CoMMonS dataset provides a challenging benchmark for fabric surface characterization.

Deep multi-level features improve material recognition accuracy.

Abstract

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.