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Fast Neighbor Search By Using Revised K-D Tree

Title:

Fast Neighbor Search By Using Revised K-D Tree

Chen, Yewang, Zhou, Lida, Tang, Yi, Singh, Jai Puneet, Bouguila, Nizar ORCID: https://orcid.org/0000-0001-7224-7940, Wang, Cheng, Wang, Huazhen and Du, Jixiang (2018) Fast Neighbor Search By Using Revised K-D Tree. Information Sciences . ISSN 00200255 (In Press)

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Official URL: http://dx.doi.org/10.1016/j.ins.2018.09.012

Abstract

We present two new neighbor query algorithms, including range query (RNN) and nearest neighbor (NN) query, based on revised k-d tree by using two techniques. The first technique is proposed for decreasing unnecessary distance computations by checking whether the cell of a node is inside or outside the specified neighborhood of query point, and the other is used to reduce redundant visiting nodes by saving the indices of descendant points. We also implement the proposed algorithms in Matlab and C. The Matlab version is to improve original RNN and NN which are based on k-d tree, C version is to improve k-Nearest neighbor query (kNN) which is based on buffer k-d tree. Theoretical and experimental analysis have shown that the proposed algorithms significantly improve the original RNN, NN and kNN in low dimension, respectively. The tradeoff is that the additional space cost of the revised k-d tree is approximately O(αnlog (n)).

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Refereed:Yes
Authors:Chen, Yewang and Zhou, Lida and Tang, Yi and Singh, Jai Puneet and Bouguila, Nizar and Wang, Cheng and Wang, Huazhen and Du, Jixiang
Journal or Publication:Information Sciences
Date:6 September 2018
Funders:
  • National Science Foundation of China
  • Science and technology project of Quanzhou City
  • Open Project Program of the National Laboratory of Pattern Recognition
  • Natural Science Foundation of Fujian Province
  • Project of science and technology plan of Fujian Province of China
Digital Object Identifier (DOI):10.1016/j.ins.2018.09.012
Keywords:k-d tree; NN; kNN; RNN
ID Code:984418
Deposited By: MONIQUE LANE
Deposited On:20 Sep 2018 20:28
Last Modified:20 Sep 2018 20:28

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