Human action classification is an important task in computer vision. Bag-of-Words using spatio-temporal features and some classification algorithm is one of the most successful methods in this context. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have used the KTH dataset to obtain a vocabulary with more descriptive words and, at the same time, more compact and efficient. Results for different vocabulary sizes show an improvement of the recognition rate whilst reducing the number of words due to the fact that non-descriptive words are removed.
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