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Data Mining for Action Recognition

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

In recent years, dense trajectories have shown to be an efficient representation for action recognition and have achieved state-of-the-art results on a variety of increasingly difficult datasets. However, while the features have greatly improved the recognition scores, the training process and machine learning used hasn’t in general deviated from the object recognition based SVM approach. This is despite the increase in quantity and complexity of the features used. This paper improves the performance of action recognition through two data mining techniques, APriori association rule mining and Contrast Set Mining. These techniques are ideally suited to action recognition and in particular, dense trajectory features as they can utilise the large amounts of data, to identify far shorter discriminative subsets of features called rules. Experimental results on one of the most challenging datasets, Hollywood2 outperforms the current state-of-the-art.

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Acknowledgement

This work was supported by the EPSRC grant “Learning to Recognise Dynamic Visual Content from Broadcast Footage” (EP/I011811/1).

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Authors and Affiliations

  1. Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey, Guildford, GU2 7XH, UK

    Andrew Gilbert & Richard Bowden

Authors
  1. Andrew Gilbert
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  2. Richard Bowden
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Corresponding author

Correspondence to Andrew Gilbert .

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Editors and Affiliations

  1. Technische Universität München, Garching, 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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Gilbert, A., Bowden, R. (2015). Data Mining for Action Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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