A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Contextual Priming for Object Detection
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Keypoint Recognition Using Randomized Trees
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Keypoint Signatures for Fast Learning and Recognition
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SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present an unconventional way of using keypoints in the form of histograms of keypoint descriptor distances. Descriptor distances are often exhaustively computed between sets of keypoints, but besides finding the k-smallest distances the structure of the distribution of these distances has been largely overlooked. We highlight the potential of such information in the task of specific scene recognition. Discriminative scene signatures in the form of histograms of keypoint descriptor distances are constructed in a supervised manner. The distances are computed between properly selected reference keypoints and the keypoints detected in the input image. The signature is low dimensional, computationally cheap to obtain, and can distinguish a large number of scenes. We introduce a scheme based on Multiclass AdaBoost to select the appropriate reference keypoints. The result is a scalable multiclass specific scene classifier capable of processing a large number of scene classes at a fraction of the time required for methods based on exhaustive keypoint matching. We test the idea on 3 datasets for specific scene recognition and report the obtained results.