Open AccessTrademark Image Retrieval Based on Faster R-CNN
Author(s) -
Wenmei Wang,
Xinxin Xu,
Zhang Jianglong,
Lifang Yang,
Gege Song,
Xianglin Huang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/3/032042
Subject(s) - trademark , computer science , image retrieval , feature (linguistics) , visual word , image (mathematics) , artificial intelligence , information retrieval , ranking (information retrieval) , object (grammar) , digital image , pattern recognition (psychology) , computer vision , image processing , linguistics , philosophy , operating system
Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.