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Multiple hierarchical deep hashing for large scale image retrieval

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Abstract

Learning-based hashing methods are becoming the mainstream for large scale visual search. They consist of two main components: hash codes learning for training data and hash functions learning for encoding new data points. The performance of a content-based image retrieval system crucially depends on the feature representation, and currently Convolutional Neural Networks (CNNs) has been proved effective for extracting high-level visual features for large scale image retrieval. In this paper, we propose a Multiple Hierarchical Deep Hashing (MHDH) approach for large scale image retrieval. Moreover, MHDH seeks to integrate multiple hierarchical non-linear transformations with hidden neural network layer for hashing code generation. The learned binary codes represent potential concepts that connect to class labels. In addition, extensive experiments on two popular datasets demonstrate the superiority of our MHDH over both supervised and unsupervised hashing methods.

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Notes

  1. During training process, we consider ‘0’ bit as ‘-1’ bit, then in the implementation of encoding and testing, we use ‘0’ again.

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Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities (Grant no. ZYGX2014J063), the National Natural Science Foundation of China (Grant no. 61502080) and the Priority Academic Program Development of Jiangsu Higher Education Institutions, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China

    Liangfu Cao, Lianli Gao, Jingkuan Song, Fumin Shen & Yuan Wang

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  1. Liangfu Cao
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  2. Lianli Gao
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  3. Jingkuan Song
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  4. Fumin Shen
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  5. Yuan Wang
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Correspondence to Lianli Gao.

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Cao, L., Gao, L., Song, J. et al. Multiple hierarchical deep hashing for large scale image retrieval. Multimed Tools Appl 77, 10471–10484 (2018). https://doi.org/10.1007/s11042-017-4489-0

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  • DOI: https://doi.org/10.1007/s11042-017-4489-0

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