Efficient Entropy-based Features Selection for Image Retrieval

被引：5

作者：

Chang, Tsun-Wei ^{[1
]}

Huang, Yo-Ping ^{[2
]}

Sandnes, Frode Eika ^{[3
]}

机构：

[1] De Lin Inst Technol, Dept Comp Sci & Informat Engn, Tucheng 236, Taipei County, Taiwan

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

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

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 | 2009年

关键词：

entropy; content-based image retrieval; feature selection; MICROARRAY;

D O I：

10.1109/ICSMC.2009.5346032

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Information retrieval systems should provide users quick access to desired information. There are no established ways for inexperienced users to explicitly express queries for retrieving images from ecological databases. This study proposes an entropy-based feature selection strategy for finding images of interest from databases. Six visual features are used to represent birds, and hence used to formulate search queries. The proposed method is tested on a real world bird database and the experimental results demonstrate the effectiveness of the presented work.

引用

页码：2941 / +

页数：2

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