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电子学报  2012 

基于随机化视觉词典组和上下文语义信息的目标检索方法

, PP. 2472-2480

Keywords: 目标检索,上下文语义信息,精确欧氏位置敏感哈希,随机化视觉词典组,K-L散度

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Abstract:

传统的视觉词典法(BagofVisualWords,BoVW)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,且当目标区域所包含的信息不能正确或不足以表达用户检索意图时就得不到理想的检索结果.针对这些问题,本文提出了基于随机化视觉词典组和上下文语义信息的目标检索方法.首先,该方法采用精确欧氏位置敏感哈希(ExactEuclideanLocalitySensitiveHashing,E2LSH)对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,利用查询目标及其周围的视觉单元构造包含上下文语义信息的目标模型;最后,引入K-L散度(Kullback-Leiblerdivergence)进行相似性度量完成目标检索.实验结果表明,新方法较好地提高了目标对象的可区分性,有效地提高了检索性能.

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