Unsupervised Trademark Retrieval Method Based on Attention Mechanism.

Jiangzhong Cao,Qingyun Dai,Wing-Kuen Ling,Yunfei Huang

doi:10.3390/s21051894

Abstract

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.

Highlights

As an important intellectual property, trademarks play an important role in social and economic development
In order to solve the problem of the high cost of data annotation and the inability to capture key information to improve trademark retrieval performance, this paper proposes an unsupervised trademark retrieval method based on channel attention
Refer to papers [1,7], this paper selects the traditional feature extraction methods commonly used in trademark retrieval, including Color Histogram (CH) [39], Local Binary

Summary

Introduction

As an important intellectual property, trademarks play an important role in social and economic development. Trademark owners register trademarks with intellectual property agencies to legalize them and protect their rights. To judge whether the trademark is infringed or not, the relevant experts evaluate the similarity of the trademark. The effective and efficient retrieval of trademarks has become the bottleneck to the management, protection, and application of trademarks. Trademark retrieval was carried out in the form of a “classification number”, which divides trademarks into different kinds manually, such a method is time-consuming and has low efficiency since the important information carrier for trademarks are images. In order to solve the problem of retrieval work, researchers began to use content-based image retrieval methods to avoid deviations caused by text descriptions, thereby capturing more accurate trademark feature information

Methods

Results

Conclusion