Building a Semantic Classification of Image Database from Patterns of Relevance Feedback

Author

Hu, Xiaohong ; Xian, Xu ; Ji, Yali ; Shi, Lei ; Wang, Qiang

Author_Institution

Sch. of Inf. & Manage. Sci., Henan Agric. Univ., Zhengzhou, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

791

Lastpage

795

Abstract

The representation of human perception has become one of the most active research topics in image retrieval. This paper proposes a novel search result clustering based relevance feedback mechanism for image retrieval, in which the value of image co-occurrence is used for mining the association of images and then the tolerance rough class is adapt to capturing the relationship among images in image database. Experimental results show that the performance of the retrieval is greatly improved and it is feasible to discover the knowledge in data obtained from relevance feedback by applying the rough set theory.

Keywords

data mining; image classification; image representation; image retrieval; pattern clustering; relevance feedback; rough set theory; visual databases; human perception representation; image association mining; image co-occurrence; image representation; image retrieval; knowledge discovery; relevance feedback pattern; search result clustering; semantic image database classification; tolerance rough set theory; Conference management; Content based retrieval; Feedback; Humans; Image databases; Image retrieval; Information retrieval; Information systems; Radio frequency; Set theory; image retrieval; relevance Feedback; rough Set; tolerance Rough Set;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.286

Filename

5193811