The nature of statistical learning theory
The nature of statistical learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Object Class Recognition using Images of Abstract Regions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
GCap: Graph-based Automatic Image Captioning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we improve the performance of a generic visual categorizer based on the ”bag of keypatches” approach using geometric information. More precisely, we consider a large number of simple geometrical relationships between interest points based on the scale, orientation or closeness. Each relationship leads to a weak classifier. The boosting approach is used to select from this multitude of classifiers (several millions in our case) and to combine them effectively with the original classifier. Results are shown on a new challenging 10 class dataset.