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.
This work has been supported by the Austrian Joint Research Project Cognitive Vision under projects S9103-N04 and S9104-N04 and the Austrian Science Fund (FWF) under the doctoral program Confluence of Vision and Graphics W1209.
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Saffari, A., Grabner, H., Bischof, H. (2008). SERBoost: Semi-supervised Boosting with Expectation Regularization. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_44
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DOI: https://doi.org/10.1007/978-3-540-88690-7_44
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