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Context Based Re-ranking for Object Retrieval

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

We propose a simple but effective re-ranking method for improving the results of object retrieval. Our method considers the contextual information embedded in a dataset. This is based on the observation that if there are multiple images containing the same object in a dataset, then these images can often be grouped into clusters. We make the following two contributions. Firstly, we gain this contextual information by a random dimension partition of the dataset. This enables online query model expansion if needed. Secondly, we use the collected contextual information to refine the initial retrieval results by taking into account the context in which each retrieved image occurs. Experimental results on several datasets demonstrate the effectiveness of our method in both accuracy and computation cost: our method refines retrieval results without relying on low-level feature matching or re-issuing the query.

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Authors and Affiliations

  1. United Technologies Research Center (China) Ltd, Room 3502, 35/F, Kerry Parkside Office, Shanghai, China

    Yanzhi Chen

  2. Australian Centre for Visual Technologies, The University of Adelaide, Adelaide, SA, 5005, Australia

    Yanzhi Chen, Anthony Dick, Xi Li & Rhys Hill

  3. College of Computer Science and Technologies, Zhejiang University, Hangzhou, China

    Xi Li

Authors
  1. Yanzhi Chen
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  2. Anthony Dick
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  3. Xi Li
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  4. Rhys Hill
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Corresponding author

Correspondence to Yanzhi Chen .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Chen, Y., Dick, A., Li, X., Hill, R. (2015). Context Based Re-ranking for Object Retrieval. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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