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Discovering characteristic landmarks on ancient coins using convolutional networks

机译:使用卷积网络在古钱币上发现特色地标

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We propose a novel method to find characteristic landmarks and recognize ancient Roman imperial coins using deep convolutional neural networks (CNNs) combined with expert-designed domain hierarchies. We first propose a new framework to recognize the Roman coin which exploits the hierarchical knowledge structure embedded in the coin domain, which we combine with the CNN-based category classifiers. We next formulate an optimization problem to discover class-specific salient coin regions. Analysis of discovered salient regions confirms that they are largely consistent with human expert annotations. Experimental results show that the proposed framework is able to effectively recognize the ancient Roman coins as well as successfully identify landmarks in a general fine-grained classification problem. For this research, we have collected a new Roman coin dataset where all coins are annotated and consist of obverse (head) and reverse (tail) images.
机译:我们提出了一种新颖的方法,可使用深度卷积神经网络(CNN)与专家设计的域层次结构相结合来查找特征性地标并识别古罗马帝国硬币。我们首先提出一个识别罗马硬币的新框架,该框架利用了嵌入在硬币域中的分层知识结构,并将其与基于CNN的类别分类器结合在一起。接下来,我们提出一个优化问题,以发现特定于类别的显着硬币区域。对发现的显着区域的分析证实,它们与人类专家注释在很大程度上是一致的。实验结果表明,该框架能够有效识别古罗马硬币,并能够成功识别一般细粒度分类问题中的地标。在这项研究中,我们收集了一个新的罗马硬币数据集,其中所有硬币都带有注释,并由正面(正面)和背面(尾部)图像组成。

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