Learning semantic cluster for image retrieval using association rule hypergraph partitioning

Author

Duan, Lijuan ; Chen, Yiqiang ; Gao, Wen

Author_Institution

Coll. of Comput. Sci., Beijing Univ. of Technol., China

Volume

3

fYear

2003

fDate

15-18 Dec. 2003

Firstpage

1581

Abstract

Semantic clustering is an important and challenging task for content-based image database management. This paper proposes a semantic clustering learning technique, which collects the relevance feedback image retrieval transaction and uses hypergraph to represent images correlation ship, then obtains the semantic clusters by hypergraph partitioning. Experiments show that it is efficient and simple.

Keywords

content-based retrieval; image retrieval; learning (artificial intelligence); pattern clustering; relevance feedback; visual databases; association rule hypergraph partitioning; content-based image database management; image extraction; image retrieval; relevance feedback; semantic clustering learning technique; Association rules; Clustering algorithms; Content based retrieval; Feedback; Humans; Image databases; Image retrieval; Information retrieval; Marine vehicles; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on

Print_ISBN

0-7803-8185-8

Type

conf

DOI

10.1109/ICICS.2003.1292733

Filename

1292733