现代电子技术2016，Vol.39Issue(23)：47-51,5.DOI:10.16652/j.issn.1004-373x.2016.23.012
自组织特征重加权结合相关反馈技术的CBIR算法
CBIR algorithm based on relevance feedback technology and self-organized feature reweighting
摘要
Abstract
To solve the problem of semantic difference between the description object of the advanced user and low⁃level image feature,a content⁃based image retrieval(CBIR)algorithm based on relevance feedback(RF)technology and self⁃organized fea⁃ture reweighting is proposed. The Gabor wavelet transform and wavelet moment technology are used to extract the image feature vectors of queried image and database image,and then the similarity is measured. In order to separate the non⁃relevance image from the relevance image to the maximum extent,the self⁃organized feature reweighting mode is introduced to ensure there is no any single relevance image in the non⁃relevance image set. The user feedback and feature weighting are conducted circularly un⁃til the user obtains a satisfactory result. The simulation experiments are performed on 1 000 images collected by Corel. The re⁃trieval accuracy of the algorithm can reach up to 97.5% for some certain images. Under the condition of no noise,the algorithm accuracy for first 10 images can reach up to 82.78%,and the accuracy for first 100 images is reduced only to 66.70%. The accu⁃racy under the noise condition is decreased by 3%. In comparison with other outstanding algorithms,this algorithm has higher accuracy and better noise robustness.关键词
图像特征/基于内容的图像检索/自组织特征重加权/Gabor小波变换/小波矩Key words
image feature/content-based image retrieval/self-organized feature reweighting/Gabor wavelet transform/wavelet moment分类
信息技术与安全科学引用本文复制引用
谭志伟,孙新领,孙挺..自组织特征重加权结合相关反馈技术的CBIR算法[J].现代电子技术,2016,39(23):47-51,5.基金项目
国家重点基础研究发展规划（973计划）前期研究专项（2011CB311802）；河南省教育厅科学技术研究重点项目（13A520221，14A520045）；河南省高等学校重点科研项目 （）
