Image Feature Attributes Reduction Based on PCA Pre-processing

被引：0
|
作者：
Chen Hai [1 ]
He Hui [1 ]
机构：
[1] Beijing Normal Univ, Coll Informat Technol, Zhuhai 519085, Guangdong, Peoples R China
来源：
ISTM/2009: 8TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6 | 2009年
关键词：
PCA; image; rough sets; reduction;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The paper discusses the application of Principal Component Analysis in image's feature attributes reduction. After PCA pre-processing, Rough Set theory was introduced, and its application in characterized parameters' attribute optimization was explored too. The unnecessary attributes were eliminated with attribute reduction algorithm. The inner redundancy of CBIR is revealed. The result of attribute reduction using UCI dataset proves the algorithm can exclude the influence of unused attributes and decrease the complexity of CBIR effectively.
引用
收藏
页码：1009 / 1013
页数：5
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