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Robust Multi-view Manifold Ranking for 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 9652))

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Abstract

Graph-based similarity ranking plays a key role in improving image retrieval performance. Its current trend is to fuse the ranking results from multiple feature sets, including textual feature, visual feature and query log feature, to elevate the retrieval effectiveness. The primary challenge is how to effectively exploit the complementary properties of different features. Another tough issue is the highly noisy features contributed by users, such as textual tags and query logs, which makes the exploration of such complementary properties difficult. This paper proposes a Multi-view Manifold Ranking (M2R) framework, in which multiple graphs built on different features are integrated to simultaneously encode the similarity ranking. To deal with the high noise issue inherent in the user-contributed features, a data cleaning solution based on visual-neighbor voting is embedded into M2R, thus called Robust M2R (RM2R). Experimental results show that the proposed method significantly outperforms the existing approaches, especially when the user-contributed features are highly noisy.

J. Wu—This work was performed when the first author was an academic visitor at University of Rochester.

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Notes

  1. 1.

    http://www.datatang.com/data/44353. The dataset was firstly used in [15].

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Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive suggestions. This work was supported in part by the ‘Natural Science Foundation of China’ (61301185, 61370070 and 61300071), the ‘Fundamental Research Funds for the Central Universities’ (2015JBM029), and the ‘Science Foundation of Beijing Jiaotong University’ (2015RC008).

Author information

Authors and Affiliations

  1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China

    Jun Wu

  2. Department of Computer Science, University of Rochester, Rochester, NY, 14627, USA

    Jun Wu, Jianbo Yuan & Jiebo Luo

Authors
  1. Jun Wu
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  2. Jianbo Yuan
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  3. Jiebo Luo
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Corresponding author

Correspondence to Jun Wu .

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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Wu, J., Yuan, J., Luo, J. (2016). Robust Multi-view Manifold Ranking for 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 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-31750-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31749-6

  • Online ISBN: 978-3-319-31750-2

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