A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation

Shuo Jiang, Jianxi Luo, Guillermo Antonio Ruiz Pava, Jie Hu, Chris Magee

Producción: Contribución a una revistaArtículo

12 Citas (Scopus)

Resumen

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
Idioma originalInglés
PublicaciónarXiv preprint arXiv:2003.08741
DOI
EstadoPublicada - 2020
Publicado de forma externa

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