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Global similarity preserving hashing

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Abstract

Hashing learning has attracted increasing attention these years with the explosive increase in data volume. Most existing hashing learning methods can be divided into two stages. Firstly, obtain low-dimensional representation of the original data. Secondly, quantize the low-dimensional representation of each sample and map them to binary codes. This two-stage hashing framework separates projection operation and quantization operation apart, and the original data structure cannot be well preserved after this kind of two-stage operation. Considering this, global similarity preserving hashing (GSPH) is proposed, which utilizes a joint hashing framework to directly project the original data to hamming space, and reduces the projection error and the quantization loss simultaneously. Moreover, GSPH presents a global similarity-based data sample reconstruction method, which describes the intrinsic manifold structure of original data more precisely. The image retrieval experimental results on Corel, CIFAR, LabelMe and NUS-WIDE datasets illustrate that our algorithm outperforms several other state-of-the-art methods.

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Acknowledgements

This study was funded by National Natural Science Foundation of Peoples Republic of China (61173163, 61370200, 61672130, 61602082) and China Postdoctoral Science Foundation (ZX20150629).

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Yang Liu, Lin Feng & Shenglan Liu

  2. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Lin Feng & Muxin Sun

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  1. Yang Liu
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  2. Lin Feng
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  3. Shenglan Liu
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  4. Muxin Sun
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Correspondence to Lin Feng.

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Yang Liu, Lin Feng, Shenglan Liu and Muxin Sun declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Di Nola.

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Liu, Y., Feng, L., Liu, S. et al. Global similarity preserving hashing. Soft Comput 22, 2105–2120 (2018). https://doi.org/10.1007/s00500-017-2683-7

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