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A Hashing Image Retrieval Method Based on Deep Learning and Local Feature Fusion

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

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Abstract

The multimedia information such as images and videos has been growing rapidly, how to efficiently retrieve large-scale image dataset to meet user needs is an urgent problem. The traditional method has the problem of slow retrieval and low accuracy on large-scale datasets, we propose an effective deep learning framework to generate binary hash codes for fast image retrieval, our idea is to fuse local features maps of different layers in convolutional neural networks (CNNs), and the binary hash codes can be learned by employing a hidden layer. Additionally, we train the network by combining cross entropy loss function with the triplet loss function to get better features. The approximate nearest neighbor search strategy is used to improve the quality and speed of retrieval. Experimental results show that our method outperforms several state-of-the-art hashing image retrieval algorithms on the MNIST and CIFAR-10 datasets. At last, we further demonstrate its scalability and efficacy on the CUB200-2011 and Stanford Dogs fine-grained classification datasets.

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Acknowledgement

This work was supported by the Grant of the National Science Foundation of China (No. 61673186, 61370006, 61502183), the Grant of the National Science Foundation of Fujian Province (No. 2013J06014, 2014J01237), the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No. ZQN-YX108), the Scientific Research Funds of Huaqiao University (No. 600005-Z15Y0016), and Subsidized Project for Cultivating Postgraduates’ Innovative Ability in Scientific Research of Huaqiao University (No. 1511314007).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Yi-Liang Nie, Ji-Xiang Du & Wen-Tao Fan

Authors
  1. Yi-Liang Nie
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  2. Ji-Xiang Du
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  3. Wen-Tao Fan
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Corresponding author

Correspondence to Ji-Xiang Du .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Nie, YL., Du, JX., Fan, WT. (2017). A Hashing Image Retrieval Method Based on Deep Learning and Local Feature Fusion. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

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