面向互联网的大规模重复图像检索技术研究  

Large-scale duplicate image retrieval technical research for the internet

作　　者：王树鹏[1] 陈明[2] 吴广君[1] 

机构地区：[1]中国科学院信息工程研究所,北京100093 [2]郑州轻工业学院软件学院,河南郑州450000

出　　处：《通信学报》2014年第12期196-202,共7页Journal on Communications

基　　金：国家自然科学基金资助项目(61271275;61202067);国家高技术研究发展计划("863"计划)基金资助项目(2013AA013205;2012AA013001;2013AA013204);北京市科技计划基金资助项目(Z131100001113034)~~

摘　　要：针对互联网上典型的社交媒体应用,提出了一个基于随机投影和分块DCT系数的大规模分布式重复图像检索方法。该方法在Hadoop集群的基础上,首先利用随机投影映射生成图像签名,再由图像签名高效的检索HBase表以获得具有高召回率的候选图像集,最后依赖分块DCT系数对候选图像进行进一步过滤来提高检索精度。实验结果表明,对于1 200万张微博图像,当H=2且T=150时,该方法的召回率为98%,精确率为93.2%,平均检索时间为6.7 s。For the typical social media application on the internet,a large-scale distributed duplicate image retrieval approach based on random projection and the block DCT coefficients was proposed.On the basis of Hadoop,this approach exploited image signatures generated by random projection mapping to retrieve HBase efficiently.And candidate images with high-recall were achieved.Then in order to improve the retrieval precision,the block DCT coefficients were used to further filter candidate images.For 12 million images,experimental results showed that with our approach the recall ratio reached 98%,the precision ratio reached 93.2%,and the average retrieval time was 6.7s when H=2 and T=150.

关 键 词：社交媒体 随机投影映射 图像签名 分块DCT系数 HADOOP集群 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]