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Supervised Class Graph Preserving Hashing for Image Retrieval and Classification

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MultiMedia Modeling (MMM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10132))

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Abstract

With the explosive growth of data, hashing-based techniques have attracted significant attention due to their efficient retrieval and storage reduction ability. However, most hashing methods do not have the ability of predicting the labels directly. In this paper, we propose a novel supervised hashing approach, namely Class Graph Preserving Hashing (CGPH), which can well incorporate label information into hashing codes and classify the samples with binary codes directly. Specifically, CGPH learns hashing functions by ensuring label consistency and preserving class graph similarity among hashing codes simultaneously. Then, it learns effective binary codes through orthogonal transformation by minimizing the quantization error between hashing function and binary codes. In addition, an iterative method is proposed for the optimization problem in CGPH. Extensive experiments on two large scale real-world image data sets show that CGPH outperforms or is comparable to state-of-the-art hashing methods in both image retrieval and classification tasks.

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Acknowledgments

This work was partially supported by National Natural Science Foundation of China (61173068, 61573212, 91546203), Program for New Century Excellent Talents in University of the Ministry of Education, Independent Innovation Foundation of Shandong Province (2014CGZH1106), Key Research and Development Program of Shandong Province (2016GGX101044, 2015GGE27033).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Lu Feng, Xin-Shun Xu, Shanqing Guo & Xiao-Lin Wang

Authors
  1. Lu Feng
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  2. Xin-Shun Xu
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  3. Shanqing Guo
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  4. Xiao-Lin Wang
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Corresponding authors

Correspondence to Xin-Shun Xu or Shanqing Guo .

Editor information

Editors and Affiliations

  1. CNRS–IRISA, Rennes, France

    Laurent Amsaleg

  2. Reykjavík University, Reykjavik, Iceland

    Gylfi Þór Guðmundsson

  3. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  4. Reykjavik University, Reykjavik, Ireland

    Björn Þór Jónsson

  5. National Institute of Informatics, Tokyo, Japan

    Shin’ichi Satoh

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Feng, L., Xu, XS., Guo, S., Wang, XL. (2017). Supervised Class Graph Preserving Hashing for Image Retrieval and Classification. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-51811-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51810-7

  • Online ISBN: 978-3-319-51811-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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