Data Mining for Action Recognition

  • Andrew Gilbert
  • Richard Bowden
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9007)


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.



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


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Centre for Vision Speech and Signal Processing (CVSSP)University of SurreyGuildfordUK

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