Machine learning tasks over image databases often generate masks that
annotate image content (e.g., saliency maps, segmentation maps) and enable a
variety of applications (e.g., determine if a model is learning spurious
correlations or if an image was maliciously modified to mislead a model). While
queries that retrieve examples based on mask properties are valuable to
practitioners, existing systems do not support such queries efficiently. In
this paper, we formalize the problem and propose a system, MaskSearch, that
focuses on accelerating queries over databases of image masks. MaskSearch
leverages a novel indexing technique and an efficient filter-verification query
execution framework. Experiments on real-world datasets with our prototype show
that MaskSearch, using indexes approximately 5% the size of the data,
accelerates individual queries by up to two orders of magnitude and
consistently outperforms existing methods on various multi-query workloads that
simulate dataset exploration and analysis processes.

本文提出了一个名为 MaskSearch 的系统，用于加速图像掩码数据库上的查询，并且通过一种新颖的索引技术和高效的查询执行框架，MaskSearch 能够在数据所占比例仅为 5% 的情况下加速两个数量级的单个查询，并在各种模拟数据集探索和分析过程的多查询负载下一致优于现有的方法。