An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data
This addresses the need for real-time, adaptive classification in streaming contexts like patient monitoring and video surveillance, though it is incremental as it builds on existing Markov switching models with hierarchical Dirichlet process priors.
The paper tackles the problem of online segmentation and classification of sequential data with an unlimited number of classes, proposing an adaptive online HDP-HMM system that achieves remarkable performance on synthetic and video datasets, particularly for evolving sequences with new classes.
In the recent years, the desire and need to understand sequential data has been increasing, with particular interest in sequential contexts such as patient monitoring, understanding daily activities, video surveillance, stock market and the like. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly, without being limited to a rigid number of classes. In addition, the model needs to be capable of updating its parameters to comply with possible evolutions. This interesting problem, however, is not adequately addressed in the literature since many studies focus on offline classification over a pre-defined class set. In this paper, we propose a principled solution to this gap by introducing an adaptive online system based on Markov switching models with hierarchical Dirichlet process priors. This infinite adaptive online approach is capable of segmenting and classifying the sequential data over unlimited number of classes, while meeting the memory and delay constraints of streaming contexts. The model is further enhanced by introducing a learning rate, responsible for balancing the extent to which the model sustains its previous learning (parameters) or adapts to the new streaming observations. Experimental results on several variants of stationary and evolving synthetic data and two video datasets, TUM Assistive Kitchen and collatedWeizmann, show remarkable performance in segmentation and classification, particularly for evolutionary sequences with changing distributions and/or containing new, unseen classes.