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» Discriminative Training for Object Recognition Using Image P...
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132
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WACV
2005
IEEE
15 years 7 months ago
Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
Andrew N. Stein, Martial Hebert
130
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CVPR
1997
IEEE
16 years 3 months ago
Pictorial Recognition Using Affine-Invariant Spectral Signatures
This paper describes an efficient approach to pose invariant object recognition employing pictorial recognition of image patches. A complete affine invariance is achieved by a rep...
Jezekiel Ben-Arie, Zhiqian Wang
125
Voted
ECCV
2008
Springer
16 years 3 months ago
Weakly Supervised Object Localization with Stable Segmentations
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
122
Voted
DAGM
2003
Springer
15 years 7 months ago
Training and Recognition of Complex Scenes Using a Holistic Statistical Model
We present a holistic statistical model for the automatic analysis of complex scenes. Here, holistic refers to an integrated approach that does not take local decisions about segme...
Daniel Keysers, Michael Motter, Thomas Deselaers, ...
NIPS
2001
15 years 3 months ago
Categorization by Learning and Combining Object Parts
We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds ...
Bernd Heisele, Thomas Serre, Massimiliano Pontil, ...