Unsupervised Trademark Retrieval Method Based on Attention Mechanism

Sensors

Qingyun Dai¹  Wing-Kuen Ling²  Jiangzhong Cao²  Yunfei Huang² 

[1] Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou 510006, China;School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China;

关键词: trademark retrieval;    instance discrimination;    attention mechanism;    local cross-channel interaction;   

DOI  :  10.3390/s21051894

来源: DOAJ

【 摘 要 】

Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and a lightweight attention mechanism is introduced to allocate a more reasonable learning weight to key features. With an unsupervised way, this proposed method can obtain good feature representation of trademarks and improve the performance of trademark retrieval. Extensive comparative experiments on the METU trademark dataset are conducted. The experimental results show that the proposed method is significantly better than traditional trademark retrieval methods and most existing supervised learning methods. The proposed method obtained a smaller value of NAR (Normalized Average Rank) at 0.051, which verifies the effectiveness of the proposed method in trademark retrieval.

【 授权许可】

Unknown