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Difficulty Guided Image Retrieval Using Linear Multiple Feature Embedding

机译：线性多特征嵌入的困难制导图像检索

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摘要

Existing image retrieval systems suffer from a performance variance for different queries. Severe performance variance may greatly degrade the effectiveness of the subsequent query-dependent ranking optimization algorithms, especially those that utilize the information mined from the initial search results. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which can predict the queries' ranking performance in terms of their difficulties and adaptively apply ranking optimization approaches. We estimate the query difficulty by comprehensively exploring the information residing in the query image, the retrieval results, and the target database. To handle the high-dimensional and multi-model image features in the large-scale image retrieval setting, we propose a linear multiple feature embedding algorithm which learns a linear transformation from a small set of data by integrating a joint subspace in which the neighborhood information is preserved. The transformation can be effectively and efficiently used to infer the subspace features of the newly observed data in the online setting. We prove the significance of query difficulty to image retrieval by applying it to guide the conduction of three retrieval refinement applications, i.e., reranking, federated search, and query suggestion. Thorough empirical studies on three datasets suggest the effectiveness and scalability of the proposed image query difficulty estimation algorithm, as well as the promising of the image difficulty guided retrieval system.
机译：现有的图像检索系统存在针对不同查询的性能差异。严重的性能差异可能会大大降低后续依赖查询的排名优化算法的效率，尤其是那些利用从初始搜索结果中提取的信息的算法。在本文中，我们通过提出一种基于查询难度的图像检索系统来解决此问题，该系统可以根据查询的难度预测查询的排名性能，并自适应地应用排名优化方法。我们通过全面探索查询图像中的信息，检索结果和目标数据库来估计查询难度。为了处理大规模图像检索环境中的高维和多模型图像特征，我们提出了一种线性多特征嵌入算法，该算法通过集成联合子空间从一小组数据中学习线性变换，在该联合子空间中，邻域信息被保留。可以有效且高效地使用该转换来推断在线设置中新观察到的数据的子空间特征。我们通过将其应用于三个检索细化应用（即重新排序，联合搜索和查询建议）的实施，证明了查询难度对图像检索的重要性。对三个数据集的全面实证研究表明，所提出的图像查询难度估计算法的有效性和可扩展性，以及图像难度指导的检索系统的前景广阔。

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