Content-based Image Retrieval for Carpet E-commerce Application

Abstract

E-commerce plays an important role in the world economy. A wide variety of websites have been designed to provide the ability of searching different types of products. Carpet is such a product which cannot be addressed easily with a special code in markets due to the huge variety in its specifications such as layout, color, and texture. This paper introduces a content-based image retrieval system for carpet e-commerce application. This system helps development of the carpet e-commerce where an image can be used instead of any tags including codes or models. An image database containing various Persian carpet images is also made for this application. Furthermore, several content-based image retrieval methods are studied and applied on the carpet database and inspiring by the evaluation results, two methods, QCLD and DCDIP are proposed for carpet e-commerce application. Simulation results show 3.1% and 2.3% decrease on the ANMRR value for the proposed QCLD and DCDIP methods respectively. Retrieval running times also are reported 2.84 and 8.15 seconds for the QCLD and DCDIP methods. In overall, these results reflect higher retrieval performance for the proposed methods

Keywords

• content-based image retrieval
• tag-based image retrieval
• carpet e-commerce
• image partitioning
• feature extraction