Applying Ensembles of Multilinear Classifiers in the Frequency Domain

Ensemble methods such as bootstrap, bagging or boosting have had a considerable impact on recent developments in machine learning, pattern recognition and computer vision. Theoretical and practical results alike have established that, in terms of accuracy, ensembles of weak classifiers generally outperform monolithic solutions. However, this comes at the cost of an extensive training process. The work presented in this paper results from a project on advanced human machine interaction. In scenarios like ours, online learning is a major requirement, and lengthy training is prohibitive. We propose a different approach to ensemble learning. Instead of a set of weak classifiers, we combine very strong, separable multilinear discriminant classifiers into an ensemble. These classifiers are especially suited for computer vision: they train very quickly and allow for rapid classification of image content. Training different classifiers for different contexts or on semantically organized data ...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Christian Bauckhage, Thomas Käster, John K. Tsotsos
• Keywords: Computer Vision, CVPR 2006, Data Provides Ensembles, Ensemble Training Schemes, Multilinear Discriminant Classifiers, Single Multilinear Function, Weak Classifiers