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Hash Learning with Convolutional Neural Networks for Semantic Based Image Retrieval

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Advances in Knowledge Discovery and Data Mining (PAKDD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9651))

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Abstract

Hashing is an effective method of approximate nearest neighbor search (ANN) for the massive web images. In this paper, we propose a method that combines convolutional neural networks (CNN) with hash learning, where the features learned by the former are beneficial to the latter. By introducing a new loss layer and a new hash layer, the proposed method can learn the hash functions that preserve the semantic information and at the same time satisfy the desirable independent properties of hashing. Experiments show that our method outperforms the state-of-the-art methods by a large margin on image retrieval. And the comparisons with baseline models show the effectiveness of our proposed layers.

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Notes

  1. 1.

    http://www.cs.toronto.edu/~kriz/cifar.html.

  2. 2.

    http://ufldl.stanford.edu/housenumbers/.

  3. 3.

    http://www.ee.columbia.edu/ln/dvmm/downloads/WeiKSHCode/dlform.htm.

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Acknowledgments

This work was supported by the National Basic Research Program (973 Program) of China (Nos. 2012CB316301 and 2013CB329403), and the National Natural Science Foundation of China (No. 61332007).

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Authors and Affiliations

  1. State Key Lab of Intelligent Technology and Systems, Tsinghua National Lab for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Jinma Guo, Shifeng Zhang & Jianmin Li

Authors
  1. Jinma Guo
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  2. Shifeng Zhang
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  3. Jianmin Li
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Corresponding author

Correspondence to Jianmin Li .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  2. The University of Texas at Dallas, Richardson, Texas, USA

    Latifur Khan

  3. Osaka University, Osaka, Japan

    Takashi Washio

  4. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  5. Shenzhen University, Shenzhen, China

    Joshua Zhexue Huang

  6. Massey University, Auckland, New Zealand

    Ruili Wang

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Guo, J., Zhang, S., Li, J. (2016). Hash Learning with Convolutional Neural Networks for Semantic Based Image Retrieval. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-31753-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31752-6

  • Online ISBN: 978-3-319-31753-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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