Speed up linear scan in high-dimensions by sorting one-dimensional projections

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Jiangtao Cui 1,* Bin Xiao 1 Gengdai Liu 1 Lian Jiang 1

1. School of Computer Science and Technology, Xidian University, Xi’an, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2011.04.06

Received: 15 Jun. 2010 / Revised: 5 Oct. 2010 / Accepted: 27 Jan. 2011 / Published: 8 Jun. 2011

Index Terms

High dimensional indexing, extended B+-tree, one-dimensional mapping, projection, distance computation


High-dimensional indexing is a pervasive challenge faced in multimedia retrieval. Existing indexing methods applying linear scan strategy, such as VA-file and its variations, are still efficient when the dimensionality is high. In this paper, we propose a new access idea implemented on linear scan based methods to speed up the nearest-neighbor queries. The idea is to map high-dimensional points into two kinds of one-dimensional values using projection and distance computation. The projection values on the line determined by the first Principal Component are sorted and indexed using a B+-tree, and the distances of each point to a reference point are also embedded into leaf node of the B+-tree. When performing nearest neighbor search, the Partial Distortion Searching and triangular inequality are employed to prune search space. In the new search algorithm, only a small portion of data points need to be linearly accessed by computing the bounded distance on the one-dimensional line, which can reduce the I/O and processor time dramatically. Experiment results on large image databases show that the new access method provides a faster search speed than existing high-dimensional index methods.

Cite This Paper

Jiangtao Cui, Bin Xiao, Gengdai Liu, Lian Jiang, "Speed up linear scan in high-dimensions by sorting one-dimensional projections", International Journal of Intelligent Systems and Applications(IJISA), vol.3, no.4, pp.41-48, 2011. DOI:10.5815/ijisa.2011.04.06


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