%0 Journal Article
%T 基于E2LSH过滤与空间一致性度量的目标检索方法
%A 赵永威
%A 李弼程
%A 彭天强
%A 唐永旺
%J 工程科学与技术
%D 2016
%R 10.15961/j.jsuese.2016.02.025
%X 中文摘要: 为了解决传统视觉词典模型（bagofvisualwordsmodel,BoVWM）中存在的时间效率低、词典区分性不强的问题，以及由于空间信息的缺失及量化误差等导致的目标检索性能较低的问题。提出一种新的目标检索方法，首先引入精确欧氏位置敏感哈希（exacteuclideanlocalitysensitivehashing,E2LSH）过滤训练图像集中的噪声和相似关键点，提高词典生成效率和质量；然后，引入卡方模型（Chi-squaremodel）移除词典中的视觉停用词增强视觉词典的区分性；最后，采用空间一致性度量准则进行目标检索并对初始结果进行K-近邻（K-nearestneighbors,K-NN）重排序。将提出的方法在数据库Oxford5K和Flickr1上进行目标检索，结果表明，新方法在一定程度上改善了视觉词典的质量，增强了视觉语义分辨能力，有效地提高目标检索性能。</br>Abstract:In order to resolve the problems of bag of visual words model(BoVWM) based object retrieval methods,such as low time efficiency,low distinction of visual words and weakly visual semantic resolution because of missing spatial information and quantization error,a novel object retrieval method was proposed.Firstly,E2LSH is used to identify and eliminate the noise key points and similar key points,consequently,the efficiency and quality of visual words was improved.Then,the stop words of dictionary were eliminated by Chi-square model to improve the distinguish ability of visual dictionary.Finally,the spatially-constrained similarity measurement was introduced to accomplish object retrieval,and a robust re-ranking method with the K-nearest neighbors of the query for automatically refining the initial search results was introduced.Experimental results on Oxford5K and Flickr1 datasets indicated that the distinguish ability of visual semantic expression is effectively improved and the object retrieval performance is substantially boosted compared with the traditional methods.
%K 目标检索 视觉词典模型 精确欧氏位置敏感哈希 空间一致性度量 卡方模型&lt
%K /br&gt
%K objectretrieval bagofvisualwordsmodel E2LSH spatially-constrainedsimilaritymeasure Chi-squaremodel
%U http://jsuese.ijournals.cn/jsuese_cn/ch/reader/view_abstract.aspx?file_no=201500214&flag=1