The Application of Similar Image Retrieval in Electronic Commerce

Author:

Hu YuPing1,Yin Hua1,Han Dezhi2,Yu Fei1

Affiliation:

1. School of Information, Guangdong University of Finance & Economics, Guangzhou 510320, China

2. College of Information Engineering, Shanghai Maritime University, Shanghai 20130, China

Abstract

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers’ experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

Funder

Guangzhou City Science and Technology Planning Project of China

Publisher

Hindawi Limited

Subject

General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Medicine

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture;Proceedings of the 2020 International Conference on Multimedia Retrieval;2020-06

2. Product image classification using Eigen Colour feature with ensemble machine learning;Egyptian Informatics Journal;2018-07

3. Less is More or Less is A Bore?;Advances in Advertising Research IX;2018

4. Retrieval Architecture with Classified Query for Content Based Image Recognition;Applied Computational Intelligence and Soft Computing;2016

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