自组织特征重加权结合相关反馈技术的CBIR算法.docVIP

自组织特征重加权结合相关反馈技术的CBIR算法 　　摘 要： 针对高级用户的描述对象与低级图像特征之间的语义差异问题，提出一种基于自组织特征重加权和相关反馈的CBIR算法。首先对查询图像和数据库图像采用Gabor小波变换和小波矩技术提取图像特征向量；然后进行相似性度量，同时为了最大程度地从相关图像中分离非相关图像，引入自组织特征重加权模式，确保非相关图像集没有单一的相关图像；最后将用户反馈和特征加权循环进行，直到得出用户满意的结果。仿真实验在Corel收集的1 000幅图像库上进行，对某些类别的图像，该算法的检索精度可高达97.5%，在无噪声情况下，对于前10幅图像，该算法的准确率为82.78%，对于前100幅图像，精度仅降到66.70%，在有噪声情况下，精度下降仅3%左右。相比其他优秀算法，该算法具有更高的精度和更好的噪声鲁棒性。 　　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 　　中图分类号： TN911.73?34； TP391.4 文献标识码： A 文章编号： 1004?373X（2016）23?0047?05 　　CBIR algorithm based on relevance feedback technology 　　and self?organized feature reweighting 　　TAN Zhiwei1， SUN Xinling1， SUN Ting2 　　（1. Department of Computer Science Technology， Henan Institute of Technology， Xinxiang 453003， China； 　　2. Institute of Visualization， Northwest University， Xi’an 710069， China） 　　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 feature 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 until the user obtains a satisfactory result. The simulation experiments are performed on 1 000 images collected by Corel. The retrieval 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 accuracy under the noise condition is decr