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
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Keypoints are high dimensional descriptors for local features of an image or an object. Keypoint extraction is the first task in various computer vision algorithms, where the keypoints are then stored in a database used as the basis for comparing images or image features. Keypoints may be based on image features extracted by feature detection operators or on a dense grid of features. Both ways produce a large number of features per image, causing both time and space performance challenges when upscaling the problem. We propose a novel framework for reducing the size of the keypoint database by learning which keypoints are beneficial for a specific application and using this knowledge to filter out a large portion of the keypoints. We demonstrate this approach on an object recognition application that uses a keypoint database. By using leave one out K nearest neighbor regression we significantly reduce the number of keypoints with relatively small reduction in performance.