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Multi-view Recognition Using Weighted View Selection

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

In this paper, we present an algorithm for multi-view recognition in a distributed camera setting that learns which viewpoints are most discriminative for particular instances of ambiguity. Our method is built on top of 2D recognition algorithms and casts view selection as the problem of optimizing kernel weights in multiple kernel learning. The main contribution is a locality-sensitive meta-training step to learn a disambiguation function to select the relative weighting of available viewpoints needed to classify a 2D input example. Our method outperforms related approaches on benchmark multi-view action recognition data sets.

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Notes

  1. 1.

    For illustration, Fig. 2 depicts a 2-dimensional feature space, but each step of our procedure can be kernelized, so a direct feature vector representation of the data is not necessary.

  2. 2.

    We were unable to directly compare these results with those previously reported. Unlike with the IXMAS set, there is little agreement in the literature on an experimental protocol for 3DO.

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Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Scott Spurlock, Hui Wu & Richard Souvenir

Authors
  1. Scott Spurlock
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  2. Hui Wu
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  3. Richard Souvenir
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Corresponding author

Correspondence to Scott Spurlock .

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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Spurlock, S., Wu, H., Souvenir, R. (2015). Multi-view Recognition Using Weighted View Selection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_35

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

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