Pivot Generation Algorithm with a Complete Binary Tree for Efficient Exact Similarity Search

Pivot Generation Algorithm with a Complete Binary Tree for Efficient Exact Similarity Search
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DOI:
10.1587/transinf.2017edp7077
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发表时间:
2018
期刊:
IEICE Trans. Inf. Syst.
影响因子:
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通讯作者:
Yuki Yamagishi;K. Aoyama;Kazumi Saito;Tetsuo Ikeda
Yuki Yamagishi;K. Aoyama;Kazumi Saito;Tetsuo Ikeda
中科院分区:
其他
文献类型:
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作者:
Yuki Yamagishi;K. Aoyama;Kazumi Saito;Tetsuo Ikeda

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This paper presents a pivot-set generation algorithm for accelerating exact similarity search in a large-scale data set. To deal with the large-scale data set, it is important to efficiently construct a search index offline as well as to perform fast exact similarity search online. Our proposed algorithm efficiently generates competent pivots with two novel techniques: hierarchical data partitioning and fast pivot optimization techniques. To make effective use of a small number of pivots, the former recursively partitions a data set into two subsets with the same size depending on the rank order from each of two assigned pivots, resulting in a complete binary tree. The latter calculates a defined objective function for pivot optimization with a low computational cost by skillfully operating data objects mapped into a pivot space. Since the generated pivots provide the tight lower bounds on distances between a query object and the data objects, an exact similarity search algorithm effectively avoids unnecessary distance calculations. We demonstrate that the search algorithm using the pivots generated by the proposed algorithm reduces distance calculations with an extremely high rate regarding a range query problem for real large-scale image data sets. key words: similarity search, pivot generation, complete binary tree