Texture Classification through Deep Residual Networks and Feature Interpretability

Abstract

Texture classification plays a critical role in various real-world and industrial applications such as material recognition in manufacturing, medical image diagnostics, surface defect detection, and agricultural monitoring. The ability to distinguish textures reliably enables automation and enhances the precision of intelligent systems. Traditional methods like Local Binary Patterns (LBP), Gabor filters, and wavelet-based descriptors have been used extensively for texture analysis. While these techniques are effective under controlled conditions, they suffer from limited robustness to changes in illumination, scale, and viewpoint. Moreover, handcrafted features often fail to capture the intricate texture structures present in real-world surfaces. The KTH-TIPS2a dataset introduces several challenges, notably large intra-class variations due to changes in scale, illumination, and pose. Additionally, the dataset includes materials with complex and fine-grained textures, making it difficult to extract discriminative features using shallow or traditional models. Addressing these challenges requires models capable of learning invariant and hierarchical representations. Deep convolutional neural networks (CNNs), such as ResNet, provide a promising solution by automatically learning multi-scale, texture-rich features that are resilient to visual variability, thereby improving classification performance on such complex datasets.