Exploring Local Graphs via Random Encoding for Texture Representation Learning

Despite many graph-based approaches being proposed to model textural patterns, they not only rely on a large number of parameters, culminating in a large search space, but also model a single, large graph for the entire image, which often overlooks fine-grained details. This paper proposes a new texture representation that utilizes a parameter-free micro-graph modeling, thereby addressing the aforementioned limitations. Specifically, for each image, we build multiple micro-graphs to model the textural patterns, and use a Randomized Neural Network (RNN) to randomly encode their topological information. Following this, the network’s learned weights are summarized through distinct statistical measures, such as mean and standard deviation, generating summarized feature vectors, which are combined to form our final texture representation. The effectiveness and robustness of our proposed approach for texture recognition was evaluated on four datasets: Outex, USPtex, Brodatz, and MBT, outperforming many literature methods. To assess the practical application of our method, we applied it to the challenging task of Brazilian plant species recognition, which requires microtex-ture characterization. The results demonstrate that our new approach is highly discriminative, indicating an important contribution to the texture analysis field.