Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval
Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval
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DOI：
10.1109/iccv.2013.214
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Cai-Zhi Zhu;H. Jégou;S. Satoh
中科院分区：
文献类型：
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作者：
Cai-Zhi Zhu;H. Jégou;S. Satoh
视觉对象检索的目的是从图像集合中检索出所有出现给定查询对象的图像。它本质上是不对称的：查询对象主要包含在数据库映像中，而匡威则不一定正确。然而，现有的方法大多是比较的图像与对称措施，没有考虑不同的角色，查询和数据库。本文首先衡量反映这一任务的大规模公共数据集的不对称程度。考虑到标准的词袋表示，然后，我们提出了新的不对称的相异占不同的内点比率与查询和数据库图像。这些非对称度量取决于查询，但它们与倒排文件结构兼容，不会明显影响搜索效率。我们的实验表明，我们的方法的好处，并表明，视觉对象检索任务是更好地处理不对称，在国家的最先进的文本检索的精神。
Visual object retrieval aims at retrieving, from a collection of images, all those in which a given query object appears. It is inherently asymmetric: the query object is mostly included in the database image, while the converse is not necessarily true. However, existing approaches mostly compare the images with symmetrical measures, without considering the different roles of query and database. This paper first measure the extent of asymmetry on large-scale public datasets reflecting this task. Considering the standard bag-of-words representation, we then propose new asymmetrical dissimilarities accounting for the different inlier ratios associated with query and database images. These asymmetrical measures depend on the query, yet they are compatible with an inverted file structure, without noticeably impacting search efficiency. Our experiments show the benefit of our approach, and show that the visual object retrieval task is better treated asymmetrically, in the spirit of state-of-the-art text retrieval.