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SSP: Supervised Sparse Projections for Large-Scale Retrieval in High Dimensions

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10111))

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Abstract

As “big data” transforms the way we solve computer vision problems, the question of how we can efficiently leverage large labelled databases becomes increasingly important. High-dimensional features, such as the convolutional neural network activations that drive many leading recognition frameworks, pose particular challenges for efficient retrieval. We present a novel method for learning compact binary codes in which the conventional dense projection matrix is replaced with a discriminatively-trained sparse projection matrix. The proposed method achieves two to three times faster encoding than modern dense binary encoding methods, while obtaining comparable retrieval accuracy, on SUN RGB-D, AwA, and ImageNet datasets. The method is also more accurate than unsupervised high-dimensional binary encoding methods at similar encoding speeds.

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Acknowledgements

We thank Yan Xia for helpful discussion. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, Vancouver, Canada

    Frederick Tung & James J. Little

Authors
  1. Frederick Tung
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  2. James J. Little
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Corresponding author

Correspondence to Frederick Tung .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Tung, F., Little, J.J. (2017). SSP: Supervised Sparse Projections for Large-Scale Retrieval in High Dimensions. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-54181-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54180-8

  • Online ISBN: 978-3-319-54181-5

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