Query Expansion (QE) is a well established method for improving retrieval metrics in image search applications. When using QE, the search is conducted on a new query vector, constructed using an aggregation function over the query and images from the database. Recent works gave rise to QE techniques in which the aggregation function is learned, whereas previous techniques were based on hand-crafted aggregation functions, e.g., taking the mean of the query's nearest neighbors. However, most QE methods have focused on aggregation functions that work directly over the query and its immediate nearest neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query, thus increasing the information used from the database when computing the query expansion, and using the structure of the nearest neighbors graph. The technique achieves state-of-the-art results over known benchmarks.
翻译:查询扩展( QE) 是改进图像搜索应用程序中检索度量的既定方法。 在使用 QE 时, 搜索是在一个新的查询矢量上进行的, 使用对查询和数据库图像的聚合功能构建。 最近的工作产生了QE 技术, 学习聚合功能, 而先前的技术则基于手工制作的聚合功能, 例如, 使用查询的近邻的平均值 。 然而, 大多数 QE 方法都侧重于对查询及其近邻直接起作用的集合功能 。 在这项工作中, 展示了一个等级模型, 图表 Query 扩展( GQE ), 以有监督的方式学习, 并在查询的宽广区域进行聚合, 从而增加计算查询扩展时从数据库中使用的信息, 并使用最近的邻居图的结构 。 该技术在已知的基准上取得了最新的结果 。