提出可持续优化的探索图，提升多媒体检索效率与探索性查询性能。
An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval
- 构建连续精炼的探索图，快速生成紧凑结构。
- 在大规模数据上实现高精度与低延迟检索，支持动态优化。
- 特别适合推荐与探索系统中的查询自包含场景。
随着数据集规模和特征向量维度的增长，大规模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与搜索速度之间表现出最佳平衡。尽管现有方法比精确搜索快几个数量级，但仍存在构建速度慢或内存开销大的问题。本文提出一种新型的‘连续精炼探索图’（crEG），可在短时间内构建出紧凑且具有顶尖搜索性能的探索图。此外，还提供可选的边优化算法进一步提升效果。两种算法均专为无向图设计，保证节点度数为偶数并始终维持图连通性，这一特性对‘探索性搜索’尤为重要——即查询本身是数据库元素之一。此类查询虽能为图搜索提供有利起点，但在传统ANNS中极少被考虑，却是推荐与探索系统的关键需求。实验表明，高效的一般性ANNS并不等同于优秀的探索性搜索性能。
原文摘要 · Abstract (English)
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".
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