A hierarchical predictive scheme for incremental time-series classification

This paper deals with the issue of gradual classification of a multivariate sequence where the number of candidate time-series generators is significantly high. It proposes a prediction scheme that consists of two components: a hierarchical structure which organizes the time-series models and a decision maker tool that assigns and evolves a respective hierarchy of probabilities; the latter expresses the current beliefs as to what the best model is for every hierarchical level. Experimentation in the domain of video-based human action recognition exhibits the capacity of the proposed approach to achieve efficient knowledge representation and real-time performance.