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An efficient LSH indexing on discriminative short codes for high-dimensional nearest neighbors

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Abstract

In massive multimedia era, the dimension curse and the I/O performance bottleneck have become two major challenges for disk-based Approximate Nearest Neighbor (ANN) search. Hashing is a popular solution to overcome the dimension curse, one promising hashing technique is Locality Sensitive Hashing (LSH). However, most existing LSH indexings incur significant I/O cost during the search due to their low NN candidate hits in each I/O access. We recommend a novel method SC-LSH (SortingCodes-LSH) which combines LSH with another hashing technique (i.e., the discriminative short codes) to lift the hit of NN candidates so as to further boost the ANN search performance. Firstly, we intensify an LSH index and sort all the compound hashing keys according to a linear order to make similar NN candidates distributed locally. Then we generate product quantization (PQ) codes to use them as candidates instead of the original data points. These space-efficient short codes can enable us acquire significantly candidates via much less I/O operations. Moreover, based on theoretical and empirical studies among series of space-filling curves, we finally choose the Gray curve as the linear order to produce better local distribution of candidate data. All these above significantly increase the NN hits during each I/O, which greatly reduce the amount of necessary I/O access. Meanwhile, with the good similarity preserving ability, PQ codes are precise enough to discriminate NNs and thus guarantee the accuracy. Empirical study demonstrates that, comparing with four state-of-the-arts, SC-LSH achieves the best accuracy with significantly smaller I/O cost and space consumption. In fact, depending on the datasets, the I/O cost (resp., space consumption) of our scheme is only 5%-20% (resp., 1%-20%) of the other methods.

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Notes

  1. In this work, we suppose that each component of the data point is a one word long integer/float.

  2. http://www.cs.princeton.edu/cass/audio.tar.gz

  3. http://corpus-texmex.irisa.fr/

  4. http://corpus-texmex.irisa.fr/

  5. http://phototour.cs.washington.edu/patches/default.htm

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61472298, 61672408, 61702403, U1135002), China 111 Project (No. B16037), China Postdoctoral Science Foundation (No. 2018M633473), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2015JQ6227), SRF for ROCS, SEM, the Fundamental Research Funds for the Central Universities (No. JB170308, etc.) and the Innovation Fund of Xidian University.

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Authors and Affiliations

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

    Feng Xiaokang & Cui Jiangtao

  2. School of Cyber Engineering, Xidian University, Xi’an, China

    Li Hui

  3. Department of System Engineering and Engineering Management, Chinese University of Hong Kong, Hong Kong, China

    Liu Yingfan

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  1. Feng Xiaokang
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  2. Cui Jiangtao
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  3. Li Hui
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  4. Liu Yingfan
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Correspondence to Cui Jiangtao.

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Xiaokang, F., Jiangtao, C., Hui, L. et al. An efficient LSH indexing on discriminative short codes for high-dimensional nearest neighbors. Multimed Tools Appl 78, 24407–24429 (2019). https://doi.org/10.1007/s11042-018-6987-0

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