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An Ensemble Hashing Framework for Fast Image Retrieval

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Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

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Abstract

Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In this paper, we attempt to exploit ensemble approaches to tackle hashing problem. A flexible ensemble hashing framework is proposed to guide the design of hashing methods, which takes into account three important principles namely higher accuracy, larger diversity and the optimal weights for predictors simultaneously. Next, a novel hashing method is designed by the proposed framework. In this work, we first use the weighted matrix to balance the variance of hash bits and then exploit bagging method to inject the diversity among hash tables. Under the same code length, the experimental results show that the proposed method achieves better performance than several other state-of-the-art methods on two image benchmarks CIFAR-10 and LabelMe.

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Acknowledgments

This research was supported by the national natural science fund (No. 61472442, 61502522, and 61502523).

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Authors and Affiliations

  1. Air Force Engineering University, Xi’an, Shanxi, China

    Huanyu Li & Yunqiang Li

Authors
  1. Huanyu Li
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  2. Yunqiang Li
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Correspondence to Huanyu Li .

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Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Li, H., Li, Y. (2018). An Ensemble Hashing Framework for Fast Image Retrieval. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

  • eBook Packages: EngineeringEngineering (R0)

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