论文标题
学习专利图像形状样本之间的空间关系
Learning Spatial Relationships between Samples of Patent Image Shapes
论文作者
论文摘要
基于二进制图像的分类和智力本质文件的检索是一个非常具有挑战性的问题。二进制图像生成机制的变化受到文档工匠设计师的影响，包括绘图样式，视图，包含多个图像组件是增加问题复杂性的合理原因。在这项工作中，我们提出了一种适合二进制图像的方法，该方法桥接了深度学习的一些成功（DL），以减轻上述变化引入的问题。该方法包括从二进制图像中提取感兴趣的形状，并应用非欧盟的几何神经网络结构来学习形状的局部和全局空间关系。经验结果表明，我们的方法在某种意义上是图像生成机制变化的不变，并且在专利图像数据集基准中实现了现有方法的表现优于现有方法。
Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem. Variations in the binary image generation mechanisms which are subject to the document artisan designer including drawing style, view-point, inclusion of multiple image components are plausible causes for increasing the complexity of the problem. In this work, we propose a method suitable to binary images which bridges some of the successes of deep learning (DL) to alleviate the problems introduced by the aforementioned variations. The method consists on extracting the shape of interest from the binary image and applying a non-Euclidean geometric neural-net architecture to learn the local and global spatial relationships of the shape. Empirical results show that our method is in some sense invariant to the image generation mechanism variations and achieves results outperforming existing methods in a patent image dataset benchmark.
