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Score-based Learning for Relevance Prediction in Image Similarity Search

机译：基于分数的学习在图像相似度搜索中的相关性预测

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摘要

Predicting the performance of queries when labels are not present has been a recurring problem faced in information retrieval systems. Beyond its clear importance, it can also be applied to aid post-retrieval optimization approaches such as re-ranking or rank-aggregation. However, most post-retrieval performance prediction approaches to retrieval systems rely on generating a single effectiveness value of performance for queries. We propose an alternative method to assess the performance of systems reliant on similarity search, which consists of predicting the individual relevance of ranked results according to the distribution of similarity scores of a given query compared to instances in a collection. The idea is that relationships between the ith ranked score and other scores of the rank can be leveraged to generate features which, in turn, are used to classify ranked objects according to their relevance to the query. We propose a positional classification scheme, in conjunction with simple and fast score-based features to predict the relevance of the top-10 results of a similarity search rank. Our results in nine scenarios, comprising three different large image datasets, show good prediction accuracy for the top-10 results, with the advantage of being amenable suitable to deploy at query time.
机译：在不存在标签的情况下预测查询的性能一直是信息检索系统中经常遇到的问题。除了其明显的重要性外，它还可用于辅助检索后优化方法，例如重新排序或排名汇总。但是，大多数检索系统的检索后性能预测方法都依赖于生成查询性能的单个有效性值。我们提出了一种替代方法来评估依赖于相似性搜索的系统性能，该方法包括根据给定查询与集合中实例的相似性得分的分布情况，预测排名结果的个体相关性。这个想法是，可以利用第i个排名得分与该排名的其他得分之间的关​​系来生成特征，这些特征又用于根据排名对象与查询的相关性对它们进行分类。我们提出一种位置分类方案，并结合基于简单和快速得分的功能来预测相似性搜索排名的前10个结果的相关性。我们在9个场景中的结果（包括三个不同的大型图像数据集）对前10个结果显示出良好的预测准确性，并且具有适合在查询时进行部署的优势。

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