Efficient descriptor tree growing for fast action recognition

•Our method significantly reduces the time complexity of a popular classifier.•Our method achieves state-of-the-art classification accuracy.•We present an efficient algorithm to organize the training data used by our method.•An important parameter of the data organization algorithm is analyzed.

Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and its classification performance is comparable to the state-of-the-art. A smart organization of training data allows the classifier to achieve reasonable computation times when working with large training databases. An efficient method for organizing training data in such a way is proposed. We perform thorough experiments on two popular action recognition datasets: the KTH dataset and the IXMAS dataset, and we study the influence of one of the key parameters of the method on classification performance.