A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation
A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation
复制标题
一种基于卷积神经网络的设计构思专利图像检索方法
DOI：
10.1115/detc2020-22048
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Magee
中科院分区：
文献类型：
--
作者：
Shuo Jiang;Jianxi Luo;Guillermo Ruiz Pava;Jie Hu;C. Magee
专利数据库因其规模大、种类多、专利文献中的设计信息丰富而常被用于对创新设计机会的灵感刺激的检索。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。该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，训练的神经网络提供了图像嵌入向量中的深度特征，这些特征可用于专利图像检索和视觉映射。训练任务和专利图像嵌入空间的准确性进行评估，以显示我们的模型的性能。这种方法也说明了机器人手臂设计检索的案例研究。与传统的基于关键字的搜索和Google图像搜索相比，该方法发现了更多对工程设计有用的视觉信息。
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.
基于深度学习的方法来预测顺序设计决策
DOI：
10.1115/detc2019-97625
发表时间：
2019
期刊：
ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
影响因子：
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作者：
Rahman, Molla Hafizur;Xie, Charles;Sha, Zhenghui
通讯作者：
Sha, Zhenghui