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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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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