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Effectiveness of NAQ-tree as index structure for similarity search in high-dimensional metric space

机译：NAQ树作为高维度量空间中相似搜索的索引结构的有效性

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摘要

Similarity search (e.g., k-nearest neighbor search) in high-dimensional metric space is the key operation in many applications, such as multimedia databases, image retrieval and object recognition, among others. The high dimensionality and the huge size of the data set require an index structure to facilitate the search. State-of-the-art index structures are built by partitioning the data set based on distances to certain reference point(s). Using the index, search is confined to a small number of partitions. However, these methods either ignore the property of the data distribution (e.g., VP-tree and its variants) or produce non-disjoint partitions (e.g., M-tree and its variants, DBM-tree); these greatly affect the search efficiency. In this paper, we study the effectiveness of a new index structure, called Nested-Approximate-eQuivalence-class tree (NAQ-tree), which overcomes the above disadvantages. NAQ-tree is constructed by recursively dividing the data set into nested approximate equivalence classes. The conducted analysis and the reported comparative test results demonstrate the effectiveness of NAQ-tree in significantly improving the search efficiency. Keywords Knn search - High dimensionality - Dimensionality reduction - Indexing - Similarity search
机译：高维度量空间中的相似性搜索（例如，k最近邻搜索）是许多应用程序中的关键操作，例如多媒体数据库，图像检索和对象识别等。数据集的高维度和巨大规模要求使用索引结构来促进搜索。通过根据到特定参考点的距离对数据集进行分区，可以构建最新的索引结构。使用索引，搜索被限制在少数几个分区中。但是，这些方法要么忽略数据分发的属性（例如VP-tree及其变体），要么产生不相交的分区（例如M-tree及其变体DBM-tree）；这些极大地影响了搜索效率。在本文中，我们研究了一种新的索引结构（称为嵌套近似eQuivalence类树（NAQ-tree））的有效性，该结构克服了上述缺点。通过将数据集递归划分为嵌套的近似等价类来构造NAQ树。进行的分析和报告的比较测试结果证明了NAQ树在显着提高搜索效率方面的有效性。关键字Knn搜索-高维-降维-索引-相似性搜索

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