一种基于随机化视觉词典组和查询扩展的目标检索方法

在目标检索领域，当前主流的解决方案是视觉词典法(Bag of Visual Words, BoVW)，然而，传统的BoVW方法具有时间效率低、内存消耗大以及视觉单词同义性和歧义性的问题。针对以上问题，该文提出了一种基于随机化视觉词典组和查询扩展的目标检索方法。首先，该方法采用精确欧氏位置敏感哈希(Exact Euclidean Locality Sensitive Hashing, E2LSH)对训练图像库的局部特征点进行聚类，生成一组支持动态扩充的随机化视觉词典组；然后，基于这组词典构建视觉词汇分布直方图和索引文件；最后，引入一种查询扩展策略完成目标检索。实验结果表明，与传统方法相比，该文方法有效地增强了目标对象的可区分性，能够较大地提高目标检索精度，同时，对大规模数据库有较好的适用性。

An Object Retrieval Method Based on Randomized Visual Dictionaries and Query Expansion

In object retrieval area,the current mainstream solution is Bag of Visual Words(BoVW) method,but there are several problems existing in the conventional BoVW methods,such as low time efficiency and large memory consumption,the synonymy and ambiguity of visual words.In this paper,a method based on randomized visual dictionaries and query expansion is proposed considering the above problems.Firstly,Exact Euclidean Locality Sensitive Hashing(E2LSH) is used to cluster local features of the training dataset,and a group of scalable randomized visual vocabularies is constructed.Then,the visual words distribution histograms and index files are created according to these randomized vocabularies.Finally,a query expansion strategy is introduced to accomplish object retrieval.Experimental results indicate that the distinguishability of objects is effectively improved and the object retrieval accuracy of the novel method is boosted dramatically compared with the classical methods,besides,it adapts large scale datasets well.