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A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation
arXiv - CS - Information Retrieval Pub Date : 2020-03-10 , DOI: arxiv-2003.08741
Shuo Jiang, Jianxi Luo, Guillermo Ruiz Pava, Jie Hu, Christopher L. Magee

The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.

中文翻译:

一种基于卷积神经网络的设计构思专利图像检索方法

专利数据库因其规模大、种类繁多、专利文件中的设计信息丰富,常被用于寻找创新设计机会的灵感刺激。然而,大多数专利挖掘研究只关注文本信息,而忽略了视觉信息。在此,我们提出了一种基于卷积神经网络 (CNN) 的专利图像检索方法。这种方法的核心是一种名为 Dual-VGG 的新型神经网络架构,旨在完成两个任务:视觉材料类型预测和国际专利分类 (IPC) 类别标签预测。反过来,经过训练的神经网络提供图像嵌入向量中的深层特征,可用于专利图像检索和视觉映射。评估训练任务和专利图像嵌入空间的准确性以显示我们模型的性能。这种方法也在机器人手臂设计检索的案例研究中得到了说明。与传统的基于关键字的搜索和谷歌图像搜索相比,所提出的方法为工程设计发现了更多有用的视觉信息。
更新日期:2020-11-06
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