DynamicBoost: Boosting time series generated by dynamical systems

René Vidal, Paolo Favaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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. ©2007 IEEE.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
DOIs
Publication statusPublished - 2007
Event2007 IEEE 11th International Conference on Computer Vision - Rio de Janeiro, Brazil
Duration: 14 Oct 200721 Oct 2007

Conference

Conference2007 IEEE 11th International Conference on Computer Vision
Abbreviated titleICCV
CountryBrazil
CityRio de Janeiro
Period14/10/0721/10/07

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  • Cite this

    Vidal, R., & Favaro, P. (2007). DynamicBoost: Boosting time series generated by dynamical systems. In Proceedings of the IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2007.4408847