DynamicBoost: Boosting Time Series Generated by Dynamical Systems

Abstract

Boosting is a remarkably simple and flexible classification algorithm with widespread applications in computer vision. However, the application of boosting to non-Euclidean, infinite length, and time-varying data, such as videos, is not straightforward. In dynamic textures, for example, the temporal evolution of image intensities is captured by a linear dynamical system, whose parameters live in a Stiefel manifold, which is clearly non-Euclidean. In this paper, we present a novel boosting method for the recognition of visual dynamical processes. Our key contribution is the design of weak classifiers (features) that are formulated as linear dynamical systems. The main advantage of such features is that they can be applied to infinitely long sequences and that they can be efficiently computed by solving a set of Sylvester equations. We also present an application of our method to dynamic texture classification.