基于集合压缩树和最佳bin优先的大数据快速KNN搜索

Zhenjie Chen, Jingqi Yan
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引用次数: 6

摘要

本文提出了基于集压缩树(SCT)和最佳bin优先(BBF)的k近邻(kNN)搜索来处理大数据问题。集合压缩树的高压缩率是通过联合压缩描述符集来实现的,而不是在每个描述符或基础上压缩。因此,集合压缩树在低比特率下具有良好的kNN搜索性能。同时,最佳bin first (BBF)算法是一种从大量高维特征描述符中寻找近似kNN的高效算法。SCT-BBF是一种新颖的探索,它从三个方面提高了搜索性能:首先,SCT-BBF对内存占用的要求更小,这在大数据时代非常重要。其次,与传统的KD-Tree方法和原始SCT方法相比,提高了精度。SCT-BBF可与PCA、SIFT等其他数据处理方法配合使用,性能更好。第三,本文采用更精细的搜索,在速度稍有损失的情况下提高准确率。而且它可以很容易地扩展到大数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast KNN search for big data with set compression tree and best bin first
This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily.
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