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. ©2007 IEEE.
|Title of host publication||Proceedings of the IEEE International Conference on Computer Vision|
|Publication status||Published - 2007|
|Event||2007 IEEE 11th International Conference on Computer Vision - Rio de Janeiro, Brazil|
Duration: 14 Oct 2007 → 21 Oct 2007
|Conference||2007 IEEE 11th International Conference on Computer Vision|
|City||Rio de Janeiro|
|Period||14/10/07 → 21/10/07|