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Deep semantic preserving hashing for large scale image retrieval

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Abstract

Hashing approaches have got a great attention because of its efficient performance for large-scale images. This paper, aims to propose a deep hashing method which can combines stacked convolutional autoencoder with hashing learning, where the input image hierarchically maps to the low dimensional space. The proposed method DCAH contains encoder-decoder, and supervisory sub-network, that generates a low dimensional binary code in a layer-wised manner of the deep conventional neural network. To optimizing the hash algorithm, we added some extra relaxations constraint to the objective function. In our extensive experiments on ultra-high dimensional image datasets, our results demonstrate that the decoder structure can improve the hashing method to preserve the similarities in hashing codes; also, DCAH achieves the best performance comparing to other states of the art approaches.

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Acknowledgments

This research was supported by NSFC, China (No: 61603171) and 863 PlanChina (No.2015AA042308).

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Authors and Affiliations

  1. Institute Image Processing & Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Masoumeh Zareapoor, Jie Yang & Pourya Shamsolmoali

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Deepak Kumar Jain

  3. Jaypee University of Engineering and Technology, Guna, India

    Neha Jain

  4. India Institute of Technology, Roorkee, India

    Surya Kant

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  1. Masoumeh Zareapoor
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Correspondence to Masoumeh Zareapoor or Jie Yang.

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Zareapoor, M., Yang, J., Jain, D.K. et al. Deep semantic preserving hashing for large scale image retrieval. Multimed Tools Appl 78, 23831–23846 (2019). https://doi.org/10.1007/s11042-018-5970-0

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