用字形聚类提升甲骨文检测，有效应对图像破损问题。
Clustering-based Feature Representation Learning for Oracle Bone Inscriptions Detection
- 基于甲骨文字库做聚类，学习更鲁棒的特征表示。
- 在两个数据集上，三种检测框架均显著提效。
- 适合数字考古、古文字识别研究者参考。
甲骨文是理解中国古代文明的关键，其从拓片图像中自动检测是数字考古的基础性挑战，主要受噪声、裂纹等退化因素影响，导致传统检测网络效果受限。为此，我们提出一种基于聚类的特征空间表示学习方法，创新性地利用甲骨文字库（OBC）作为先验知识，通过聚类结果优化检测网络的特征提取能力。该方法设计了一种基于聚类结果的专用损失函数，融入整体网络损失进行联合优化。我们在两个甲骨文检测数据集上，采用Faster R-CNN、DETR和Sparse R-CNN三种主流检测框架进行了实验验证。大量实验表明，所有框架均取得显著性能提升。
原文摘要 · Abstract (English)
Oracle Bone Inscriptions (OBIs), play a crucial role in understanding ancient Chinese civilization. The automated detection of OBIs from rubbing images represents a fundamental yet challenging task in digital archaeology, primarily due to various degradation factors including noise and cracks that limit the effectiveness of conventional detection networks. To address these challenges, we propose a novel clustering-based feature space representation learning method. Our approach uniquely leverages the Oracle Bones Character (OBC) font library dataset as prior knowledge to enhance feature extraction in the detection network through clustering-based representation learning. The method incorporates a specialized loss function derived from clustering results to optimize feature representation, which is then integrated into the total network loss. We validate the effectiveness of our method by conducting experiments on two OBIs detection dataset using three mainstream detection frameworks: Faster R-CNN, DETR, and Sparse R-CNN. Through extensive experimentation, all frameworks demonstrate significant performance improvements.
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