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首页> 外文期刊>Signal Processing Magazine, IEEE >Ancient Coin Classification Using Reverse Motif Recognition: Image-based classification of Roman Republican coins
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Ancient Coin Classification Using Reverse Motif Recognition: Image-based classification of Roman Republican coins

机译：使用逆序图案识别的古钱币分类：基于图像的罗马共和党钱币分类

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摘要

We propose a holistic system to classify ancient Roman Republican coins based on their reverse-side motifs. The bag-of-visual-words (BoW) model is enriched with spatial information to increase the discriminative power of the coin image representation. This is achieved by combining a spatial pooling scheme with co-occurrence encoding of visual words. We specifically address the required geometric invariance properties of image-based ancient coin classification, as coins from different collections can be located at differing image locations, have various scales in the images and can undergo various in-plane rotations. We evaluate our method on a data set of 2,224 coin images from three different sources. The experimental results show that our proposed image representation is more discriminative than the traditional bag-of-visual-words model while still being invariant to the mentioned geometric transformations. For 29 motifs, the system achieves a classification rate of 82%. It is considered to act as a helpful tool for numismatists in the near future, which facilitates and supports the traditional coin classification process by a faster presorting of coins.
机译：我们提出了一个整体系统，根据其背面图案对古罗马共和党硬币进行分类。视觉词袋（BoW）模型丰富了空间信息，以提高硬币图像表示的判别力。这是通过将空间池化方案与视觉单词的同时出现编码相结合来实现的。我们特别解决了基于图像的古代硬币分类所需的几何不变性属性，因为来自不同集合的硬币可以位于不同的图像位置，图像中具有各种比例，并且可以进行各种平面内旋转。我们在来自三个不同来源的2,224个硬币图像的数据集上评估了我们的方法。实验结果表明，我们提出的图像表示比传统的视觉词袋模型更具判别力，同时仍然不变地提到了几何变换。对于29个图案，该系统的分类率为82％。它被认为是在不久的将来对货币学家有用的工具，它通过更快的硬币预分类来促进和支持传统的硬币分类过程。

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