Image Feature Attributes Reduction Based on PCA Pre-processing
The paper discusses the application of Principal Component Analysis in images 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.
PCA image rough sets reduction
CHEN Hai HE Hui
College of Information Technology, Beijing Normal University Zhuhai Campus, Zhuhai, Guangdong, 519085
国际会议
第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)
重庆
英文
1009-1013
2009-08-01（万方平台首次上网日期，不代表论文的发表时间）