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arXiv · 1405.7545

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

Michael Sapienza·Fabio Cuzzolin·Philip H. S. Torr
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Abstract

The recent trend in action recognition is towards larger datasets, an increasing number of action classes and larger visual vocabularies. State-of-the-art human action classification in challenging video data is currently based on a bag-of-visual-words pipeline in which space-time features are aggregated globally to form a histogram. The strategies chosen to sample features and construct a visual vocabulary are critical to performance, in fact often dominating performance. In this work we provide a critical evaluation of various approaches to building a vocabulary and show that good practises do have a significant impact. By subsampling and partitioning features strategically, we are able to achieve state-of-the-art results on 5 major action recognition datasets using relatively small visual vocabularies.

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Michael Sapienza, Fabio Cuzzolin, Philip H. S. Torr. 2014-05-29. Feature sampling and partitioning for visual vocabulary generation on large action classification datasets. https://arxiv.org/abs/1405.7545

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