GraphTEN: Graph Enhanced Texture Encoding Network

Abstract--Texture recognition is a fundamental problem in computer vision and pattern recognition. Recent progress leverages feature aggregation into discriminative descriptions based on convolutional neural networks (CNNs). However, modeling non-local context relations through visual primitives remains challenging due to the variability and randomness of texture primitives in spatial distributions. Texture, as a fundamental visual attribute, encapsulates Building upon these foundations, recent research has continued the spatial organization of basic elements within texture-rich to advance texture representation and recognition by images, serving as a vital representation of the underlying exploring innovative perspectives. Textured regions are typically propose a learnable Gabor-based framework that integrates characterized by repetitive patterns with inherent variability, trainable statistical feature extractors with deep neural networks making them essential pre-attentive visual cues for comprehending to enhance fine-grained texture recognition.