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Fast graph similarity search via hashing and its application on image retrieval

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Abstract

Similarity search in graph databases has been widely investigated. It is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we investigate a k-NN (k-Nearest Neighbor) similarity search problem by locality sensitive hashing (LSH). We propose an innovative fast graph search algorithm named LSH-GSS, which first transforms complex graphs into vectorial representations based on prototypes in the database and later accelerates a query in Euclidean space by employing LSH. Because images can be represented as attributed graphs, we propose an approach to transform attributed graphs into n-dimensional vectors and apply LSH-GSS to execute further image retrieval. Experiments on three real graph datasets and two image datasets show that our methods are highly accurate and efficient.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61370125 and 61402026), SKLSDE-2017ZX-03.

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Authors and Affiliations

  1. State Key Lab of Software Development Environment, BeiHang University, Beijing, China

    Bo Lang, Bo Wu, Yang Liu, Xianglong Liu & Boyu Zhang

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  1. Bo Lang
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  2. Bo Wu
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  3. Yang Liu
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  4. Xianglong Liu
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  5. Boyu Zhang
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Correspondence to Bo Wu.

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Lang, B., Wu, B., Liu, Y. et al. Fast graph similarity search via hashing and its application on image retrieval. Multimed Tools Appl 77, 16177–16198 (2018). https://doi.org/10.1007/s11042-017-5194-8

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  • DOI: https://doi.org/10.1007/s11042-017-5194-8

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