用图结构建模纹理局部与全局关系，提升识别精度。
GraphTEN: Graph Enhanced Texture Encoding Network
- 构建全连接图与二分图捕捉纹理的全局关联与跨尺度依赖
- 在五个公开数据集上超越现有方法，实现更优纹理识别性能
- 适合关注纹理建模与图像理解的研究者
纹理识别是计算机视觉与模式识别中的基础问题。尽管近期方法通过卷积神经网络（CNN）进行特征聚合以生成判别性描述，但如何通过视觉原型建模非局部上下文关系仍具挑战，尤其因纹理原型的空间分布具有多样性和随机性。本文提出图增强纹理编码网络（GraphTEN），旨在捕捉纹理原型的局部与全局特征。GraphTEN通过全连接图建模全局关联，并利用二分图捕获纹理原型的跨尺度依赖。此外，引入补丁编码模块，采用码本将多尺度补丁特征编码至统一特征空间，实现无序纹理表示。所提方法在五个公开数据集上均优于当前最优方法，表现显著提升。
原文摘要 · Abstract (English)
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. In this paper, we propose a graph-enhanced texture encoding network (GraphTEN) designed to capture both local and global features of texture primitives. GraphTEN models global associations through fully connected graphs and captures cross-scale dependencies of texture primitives via bipartite graphs. Additionally, we introduce a patch encoding module that utilizes a codebook to achieve an orderless representation of texture by encoding multi-scale patch features into a unified feature space. The proposed GraphTEN achieves superior performance compared to state-of-the-art methods across five publicly available datasets.
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