Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection

View Sample PDF

Author(s): Hao Wu (Beijing Normal University, China), Zhiyi Zhang (Beijing Normal University, China)and Zhilin Zhu (Shandong Technology and Business University, China)
Copyright: 2024
Volume: 36
Issue: 1
Pages: 12
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.335455

Keywords: Computer Science & IT / End-User Computing / Human-Computer Interaction / Information Science Reference

Purchase

View Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection on the publisher's website for pricing and purchasing information.

Abstract

A trademark is an essential symbol of a company, consisting of a semantically rich image under ordinary circumstances. The popularity of a company can be measured by the frequency of its trademark being used. Therefore, efficiently retrieving trademark images would directly contribute to the detection of popular companies. However, most mainstream retrieval methods are not especially pertinent to trademark image retrieval. To solve this problem, a combination of the ResNet50 network and Autoencoder with local sensitive hashing (LSH) is used to conduct full cross-checking, which significantly improves the effectiveness of trademark image retrieval. Meanwhile, image super-resolution-based sparse coding is also proposed to achieve high-precision trademark image retrieval and its effect is particularly significant for challenging trademark images. Finally, the authors conduct extensive experiments on a high-quality database to demonstrate the substantial effectiveness of the proposed methods.

The IRMA Community

Research IRM

Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection

Purchase

Abstract

Related Content

IRMA Sponsors