AI 中文总结
本文提出连续求精探索图（crEG），用于快速构建紧凑图以实现高效多媒体检索，其偶度无向图及连通性特性适配探索式搜索，实验显示ANNS高效率与探索式搜索性能无必然关联。
AI 中文摘要
随着数据集和特征向量维度的不断增长，大型多媒体数据库中的近似最近邻搜索（ANNS）变得愈发重要。基于图的方法在检索精度和搜索时间之间展现出最佳权衡，尽管其搜索时间可比精确搜索技术快数个数量级，但现有方法存在构建速度慢或内存需求高的问题。本文提出一种“连续求精探索图（crEG）”，这是一种用于快速构建紧凑探索图的新方法，具备最先进的搜索性能，还可通过可选的边优化算法进一步提升有效性。两种算法均专门设计用于生成和操作偶度无向图，并始终保证图的连通性，该特性对“探索式搜索”尤为重要——在该场景中，查询是数据库元素的一部分。尽管此类查询为图搜索算法提供了有利的起点，但在ANNS领域中却很少被考虑，而它们对推荐和探索系统至关重要。我们的实验表明，ANNS的高效率并不一定能转化为“探索式搜索”的良好性能。
英文摘要
As datasets and the dimensionality of feature vectors continue to grow, Approximate Nearest Neighbor Search (ANNS) in large multimedia databases becomes increasingly relevant. Graph-based approaches have demonstrated to offer the best trade-off between retrieval precision and search time. Despite their ability to deliver search times several orders of magnitude faster than exact search techniques, existing methods suffer from slow constructions speeds or high memory requirements. This paper presents a "continuous refining Exploration Graph" (crEG), a novel approach for rapidly constructing a compact exploration graph with state-of-the-art search performance. Additionally, it provides the ability to enhance its effectiveness even further through an optional edge optimization algorithm. Both algorithms are specifically designed to produce and operate on undirected graphs with even degrees and guarantee graph connectivity at any time, a property particularly valuable for "exploratory search", where the query is part of the database elements. Although such queries provide an advantageous starting point for graph search algorithms, they have been rarely considered in the context of ANNS, yet are crucial for recommendation and exploration systems. Our experiments demonstrate high efficiency in ANNS does not necessarily translate to a good performance in "exploratory search".
Journal refProc. ICMR 2024, pp. 1-10
