The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Saliency, Scale and Image Description
International Journal of Computer Vision
Digital Image Processing
Linear Programming Boosting via Column Generation
Machine Learning
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Object Recognition Using Segmentation for Feature Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a multiclass classification system for gray value images through boosting. The feature selection is done using the LPBoost algorithm which selects suitable features of adequate type. In our experiments we use up to nine different kinds of feature types simultaneously. Furthermore, a greedy search strategy within the weak learner is used to find simple geometric relations between selected features from previous boosting rounds. The final hypothesis can also consist of more than one geometric model for an object class. Finally, we provide a weight optimization method for combining the learned one-vs-one classifiers for the multiclass classification. We tested our approach on a publicly available data set and compared our results to other state-of-the-art approaches, such as the ”bag of keypoints” method.