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.