1. A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation
- Author
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Shuo Jiang, Jianxi Luo, Christopher L. Magee, Guillermo Ruiz Pava, and Jie Hu
- Subjects
FOS: Computer and information sciences ,Class (computer programming) ,Artificial neural network ,Computer science ,business.industry ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine learning ,computer.software_genre ,Convolutional neural network ,Computer Science - Information Retrieval ,Embedding ,Artificial intelligence ,International Patent Classification ,business ,Engineering design process ,Robotic arm ,computer ,Image retrieval ,Information Retrieval (cs.IR) - Abstract
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., 11 pages, 11 figures
- Published
- 2021