Generic object recognition with boosting

IEEE Transactions on Pattern Analysis and Machine Intelligence
Andreas OpeltPeter Auer

Abstract

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 all comparable solutions on common databases.

References

Feb 6, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·F J González-CastañoJ M Pousada-Carballo

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Citations

Dec 21, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Giovanni GualdiRita Cucchiara
May 27, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jamshid SouratiDana H Brooks
Jan 17, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Thomas SerreTomaso Poggio
Jul 4, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Matthew Toews, Tal Arbel
Aug 21, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Wei FengZhi-Qiang Liu
Apr 18, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Dashan GaoNuno Vasconcelos
Aug 8, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Xinwang LiuJian Zhang
Oct 16, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Wenjing LiNikolaos G Bourbakis
Sep 29, 2009·IEEE Transactions on Medical Imaging·Szu-Hao HuangCarol L Novak
Jul 20, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society· Yongzhen HuangXuelong Li
Dec 16, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhicheng Li, Laurent Itti
Apr 14, 2016·Cerebral Cortex·Christopher BaldassanoLi Fei-Fei
May 12, 2016·Journal of Cognitive Neuroscience·Sabrina Fagioli, Emiliano Macaluso
Jan 24, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jimei Yang, Ming-Hsuan Yang
Jan 17, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jiansheng ChenBo Huang
Jan 1, 2019·PLoS Computational Biology·Iris I A GroenH Steven Scholte
Jul 1, 2020·Scientific Reports·Noor SeijdelH Steven Scholte

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