A Comparison of Content Based Image Retrieval Systems

被引：0

作者：

Wang, Yuhan ^{[1
]}

Li, Qiaochu ^{[2
]}

Lan, Tian ^{[3
]}

Chen, James ^{[4
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu Coll, Commun Engn, Chengdu, Peoples R China

[2] Dalian Univ Technol, Sch Humanities & Social Sci, Dalian, Peoples R China

[3] Guangzhou Math Modeling Practice Base, Guangzhou, Guangdong, Peoples R China

[4] Univ Southern Calif, Dept Comp Sci, Los Angeles, CA USA

来源：

2014 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) | 2014年

关键词：

Content-based image retrieval; QBVE; QBSE; MUVIS; semantic retrieval; QUERY;

D O I：

10.1109/CSE.2014.143

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Content-based image retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem. There are two main content-based image retrieval paradigms: one based on visual queries, referred to as query-by-visual-example (QBVE), and the other based on semantic content, denoted as semantic retrieval. In this paper, we compare these two kinds of retrieval systems by conducting experiments on two real typical content-based image retrieval systems: MUVIS and QBSE. The experiments show that QBSE has a better performance than MUVIS, which belongs to QBVE. Semantic space is the most important factor for QBSE system.

引用

页码：669 / 673

页数：5

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