基于多探寻局部敏感哈希和单词映射链投票的图像检索方法  

Image Retrieval Based on Multi-probe Locality Sensitive Hashing and Word Map Chain Voting

作　　者：许喆[1] 陈福才[1] 李邵梅[1] 李星[1] 

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002

出　　处：《计算机科学》2014年第5期82-85,106,共5页Computer Science

基　　金：国家863计划项目(2011AA010603)资助

摘　　要：为解决基于欧氏局部敏感哈希(E2LSH)的视觉词典法存在的内存消耗大、在图像背景明显变化时检索精度不高及增大数据库规模导致检索效率降低的问题,在采用多探寻LSH对特征点进行聚类的基础上提出的基于嵌入汉明码的单词映射链投票的图像检索方法。该方法首先采用多单词映射和软量化思想构造单表视觉词典,缩小词典规模以降低内存消耗;然后通过嵌入汉明码生成单词映射链,并提出一种权重赋予函数来增加检索精度;最后对匹配返回的单词映射链进行加权投票完成图像检索。实验结果表明,该方法能有效降低检索的内存消耗,提高检索精度,且适用于大规模数据库条件下的检索处理。To solve the problem of high memory cost,low retrieval accuracy when the background is changed obviously and reduced efficiency when the size of the database is increased of the BoVW （bag of visual words） method based on Euclidean locality sensitive hashing （E2LSH）,a fast retrieval method based on word map chain voting of Hamming embedding was presented on the basis of clustering the feature points through multi-probe locality sensitive hashing.The method constructs a single-table visual dictionary with multi-word mapping and soft assignment,reduces the size of the dictionary to reduce memory consumption.Then a word map chain is constructed with Hamming embedding,and a weighting function is proposed to increase the retrieval accuracy.A weighted voting of the word map chain from matching results is made to accomplish image retrieval.Experimental results show that this method can effectively reduce memory consumption,improve retrieval accuracy,and is applicable to large scale datasets.

关 键 词：图像检索 多探寻局部敏感哈希 嵌入汉明码 单词映射链 视觉词典 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]