SERBoost: Semi-supervised Boosting with Expectation Regularization

Authors:
Amir Saffari;Helmut Grabner;Horst Bischof
Affiliations:
Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria and Computer Vision Laboratory, ETH Zurich, Switzerland;Institute for Computer Graphics and Vision, Graz University of Technology, Austria
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Year:
2008

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Abstract

The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems. The complexity is mainly dominated by the base learners. The algorithm provides a margin regularizer for the boosting cost function and shows a principled way of utilizing prior knowledge. We demonstrate the performance of SERBoost on the Pascal VOC2006 set and compare it to other supervised and semi-supervised methods, where SERBoost shows improvements both in terms of classification accuracy and computational speed.