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Visual Re-ranking Through Greedy Selection and Rank Fusion

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

Image search re-ranking has proven its effectiveness in the text-based image search system. However, traditional re-ranking algorithm heavily relies on the relevance of the top-ranked images. Due to the huge semantic gap between query and the image, the text-based retrieval result is unsatisfactory. Besides, single re-ranking model has large variance and is easy to over-fit. Instead, multiple re-ranking models can better balance the biased and the variance. In this paper, we first conduct label de-noising to filter false-positive images. Then a simple greedy graph-based re-ranking algorithm is proposed to derive the resulting list. Afterwards, different images are chosen as the seed images to perform re-ranking multiple times. Using the rank fusion, the results from different graphs are combined to form a better result. Extensive experiments are conducted on the INRIA web353 dataset and demonstrate that our method achieves significant improvement over state-of-the-art methods.

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Acknowledgment

This work was supported by the 973 project under Contract 2015CB351803, by the NSFC under Contracts 61390514 and 61201413, by the Youth Innovation Promotion Association CAS No. CX2100060016, by the Fundamental Research Funds for the Central Universities No. WK2100060011 and No. WK2100100021, and by the Specialized Research Fund for the Doctoral Program of Higher Education No. WJ2100060003.

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Authors and Affiliations

  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, China

    Bin Lin, Ai Wei & Xinmei Tian

Authors
  1. Bin Lin
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  2. Ai Wei
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  3. Xinmei Tian
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Corresponding author

Correspondence to Xinmei Tian .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Lin, B., Wei, A., Tian, X. (2016). Visual Re-ranking Through Greedy Selection and Rank Fusion. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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