Dimension reduction based on rough set in image mining

Author

Liu, Maofu ; He, Yanxiang ; Hu, Huijun ; Yu, Dandan

Author_Institution

Sch. of Comput., Wuhan Univ., China

fYear

2004

fDate

14-16 Sept. 2004

Firstpage

39

Lastpage

44

Abstract

Image mining is a nontrivial process to discover valid, novel, potentially useful, and ultimately understandable knowledge from large image sets or image databases. With the rapid development of rough set theory in recent years, more and more people have applied this theory to different research fields. In order to solve the curse of dimensionality in image mining, in this paper, we give our own solution to this problem based on rough set. After introducing the basic concepts of rough set theory and the attribute reduction of information system, we put forward the related algorithms mainly including the partition algorithm and the dimension reduction algorithm. The experimental result has justified the feasibility of the solution based on rough set.

Keywords

data mining; rough set theory; visual databases; attribute reduction; dimension reduction algorithm; image databases; image mining; image understanding; information system; large image sets; partition algorithm; rough set theory; Artificial intelligence; Data mining; Digital images; Helium; Image databases; Information systems; Partitioning algorithms; Principal component analysis; Set theory; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Technology, 2004. CIT '04. The Fourth International Conference on

Print_ISBN

0-7695-2216-5

Type

conf

DOI

10.1109/CIT.2004.1357172

Filename

1357172