Generic Object Recognition with Boosting

9 years 10 months ago
Generic Object Recognition with Boosting
This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of either discontinuity or homogeneity. A variety of local descriptors can be applied to form a set of feature vectors for each local region. Boosting is used to learn a subset of such feature vectors (weak hypotheses) and to combine them into one final hypothesis for each visual category. This combination of individual extractors and descriptors leads to recognition rates that are superior to other approaches which use only one specific extractor/descriptor setting. To explore the limitation of our system, we had to set up new, highly complex image databases that show the objects of interest at varying scales and poses, in cluttered background, and under considerable occlusion. We obtain classification results up to 81 percent ROC-equal error rate on the most complex of our databases. Our approach outperforms al...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Andreas Opelt, Axel Pinz, Michael Fussenegger, Peter Auer
Comments (0)