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The recent trend in action recognition is towards larger datasets, anincreasing number of action classes and larger visual vocabularies.State-of-the-art human action classification in challenging video data iscurrently based on a bag-of-visual-words pipeline in which space-time featuresare aggregated globally to form a histogram. The strategies chosen to samplefeatures and construct a visual vocabulary are critical to performance, in factoften dominating performance. In this work we provide a critical evaluation ofvarious approaches to building a vocabulary and show that good practises dohave a significant impact. By subsampling and partitioning featuresstrategically, we are able to achieve state-of-the-art results on 5 majoraction recognition datasets using relatively small visual vocabularies.

Feature sampling and partitioning for visual vocabulary generation on large action classification datasets