NDLI: Applying Ensembles of Multilinear Classifiers in the Frequency Domain

Content Provider	IEEE Xplore Digital Library
Author	Bauckhage, C. Kaster, T. Tsotsos, J.K.
Copyright Year	2006
Description	Author affiliation: Deutsche Telekom Laboratories, Germany (Bauckhage, C.)
Abstract	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 projects on advanced human machine interaction. In scenarios like ours, online learning is a major requirement, and lengthy training is prohibitive. We therefore propose a different approach to ensemble learning. Instead of a set of weak classifiers, we combine strong, separable, multilinear discriminant functions. These 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 provides ensembles of experts. We collapse a set of experts into a single multilinear function and thus achieve the same runtime for arbitrarily many classifiers as for a single one. Moreover, carrying out the classification in the frequency domain results in faster framerates. Experiments with image sequences recorded in typical home environments show that our ensemble training schemes yield high accuracy on unconstrained and cluttered data.
Starting Page	95
Ending Page	102
File Size	1141867
Page Count	8
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.59
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency domain analysis Computer vision Bagging Boosting Machine learning Pattern recognition Costs Humans Runtime Image sequences
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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