Efficient shape-based image retrieval based on gray relational analysis and association rules

被引：5

作者：

Huang, Yo-Ping ^{[1
]}

Chang, Tsun-Wei ^{[2
]}

Sandnes, Frode Eika ^{[3
]}

机构：

[1] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan

[2] De Ling Inst Technol, Dept Comp Sci & Informat Engn, Taipei 236, Taipei County, Taiwan

[3] Oslo Univ Coll, Fac Engn, Oslo, Norway

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2008年 / 22卷 / 04期

关键词：

shape-based image retrieval; gray theory; GM(1; N); relevance feedback; association rules;

D O I：

10.1142/S021800140800648X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An improved shape based image retrieval strategy based on gray relational analysis and association rules is proposed. The choice of a suitable object representation and retrieval scheme is essential for efficient retrieval. In addition, a two-stage relevance feedback mechanism based on the GM(1, N) method and association rules is incorporated to improve the retrieval accuracy. The GM(1, N) method is used to build the re-query example for subsequent retrievals. The retrieval log files stored on the server are used for offline mining of association rules. The association rules mined from users' retrieval history can further reveal users' image searching behavior. The effectiveness of the proposed model is demonstrated on the FISH dataset.

引用

页码：711 / 732

页数：22

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