GraphTEN: Graph Enhanced Texture Encoding Network
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908273178836992 |
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| author | Peng, Bo Chen, Jintao Yao, Mufeng Zhang, Chenhao Zhang, Jianghui Chi, Mingmin Tao, Jiang |
| author_facet | Peng, Bo Chen, Jintao Yao, Mufeng Zhang, Chenhao Zhang, Jianghui Chi, Mingmin Tao, Jiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraphTEN: Graph Enhanced Texture Encoding Network Peng, Bo Chen, Jintao Yao, Mufeng Zhang, Chenhao Zhang, Jianghui Chi, Mingmin Tao, Jiang Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.2.10; I.4.7 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. |
| title | GraphTEN: Graph Enhanced Texture Encoding Network |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.2.10; I.4.7 |
| url | https://arxiv.org/abs/2503.13991 |