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Generic Object Recognition with Boosting
March 2006 (vol. 28 no. 3)
pp. 416-431
ASCII Text
x 
 
Andreas Opelt, Axel Pinz, Michael Fussenegger, Peter Auer, "Generic Object Recognition with Boosting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 416-431, March, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.54,
author = {Andreas Opelt and Axel Pinz and Michael Fussenegger and Peter Auer},
title = {Generic Object Recognition with Boosting},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {3},
issn = {0162-8828},
year = {2006},
pages = {416-431},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.54},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Generic Object Recognition with Boosting
IS - 3
SN - 0162-8828
SP416
EP431
EPD - 416-431
A1 - Andreas Opelt,
A1 - Axel Pinz,
A1 - Michael Fussenegger,
A1 - Peter Auer,
PY - 2006
KW - Boosting
KW - object categorization
KW - object localization.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Boosting, object categorization, object localization.
Citation:
Andreas Opelt, Axel Pinz, Michael Fussenegger, Peter Auer, "Generic Object Recognition with Boosting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 416-431, March 2006, doi:10.1109/TPAMI.2006.54
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