用巴氏距离构建特征流形，提升波斯萨珊金币分类准确率
EigenCoin: sassanid coins classification based on Bhattacharyya distance
- 基于巴氏距离构建特征流形，融合整体与局部特征
- 分类准确率提升9.45%至21.75%，有效缓解过拟合
- 适用于小样本、不平衡的文物图像分类任务
利用不平衡数据集解决模式识别问题是当前研究热点。本文聚焦于萨珊金币分类任务，提出EigenCoin流形方法并结合巴氏距离进行分类。该方法包含三个步骤：流形构建、测试数据映射与分类。实验表明，EigenCoin在多种算法中表现最优，准确率提升9.45%至21.75%，同时具备良好的抗过拟合能力。研究还对比了整体特征与局部特征两种方法的影响，验证了其有效性。
原文摘要 · Abstract (English)
Solving pattern recognition problems using imbalanced databases is a hot topic, which entices researchers to bring it into focus. Therefore, we consider this problem in the application of Sassanid coins classification. Our focus is not only on proposing EigenCoin manifold with Bhattacharyya distance for the classification task, but also on testing the influence of the holistic and feature-based approaches. EigenCoin consists of three main steps namely manifold construction, mapping test data, and classification. Conducted experiments show EigenCoin outperformed other observed algorithms and achieved the accuracy from 9.45% up to 21.75%, while it has the capability of handling the over-fitting problem.
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