Improving Texture Recognition via Multi-Layer Feature Aggregation from Pre-Trained Vision Architectures

Texture recognition is a fundamental task in computer vision, with diverse applications in material sciences, medicine, and agriculture. The ability to analyze complex patterns in images has been greatly enhanced by advancements in Deep Neural Networks and Vision Transformers. To address the challenging nature of texture recognition, this paper investigates the performance of several pre-trained vision architectures for texture recognition, including both CNN- and transformer-based models. For each architecture, multi-level features are extracted from early, intermediate, and final layers, concatenated, and fed into a trainable Multi-Layer Perceptron (MLP) classifier. The architecture is thoroughly evaluated using five publicly available texture datasets, KTH-TIPS2-b, FMD, GTOS-Mobile, DTD, and Soil, with MLP hyperparameters determined through an exhaustive grid search on one of the datasets to ensure optimal performance. Extensive experiments highlight the comparative performance of each architecture and demonstrate that aggregating features from different hierarchical levels improves texture recognition in most cases, outperforming even architectures that require substantially higher computational resources. The study also shows the particular effectiveness of transformer-based models, such as BEiTv2, in achieving state-of-the-art results on four of the five examined datasets.