Visual words selection for human action classification

Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with more descriptive words where the representation is more compact and efficient. Two feature descriptors, STIP and MoSIFT, and two classifiers, KNN and SVM, have been used to check the validity of our approach. Results for different vocabulary sizes show an improvement of the recognition rate whilst reducing the number of words as non-descriptive words are removed. Additionally, state-of-the-art performances are reached with this new compact vocabulary representation.