Keypoint induced distance profiles for visual recognition

We show that histograms of keypoint descriptor distances can make useful features for visual recognition. 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 particular 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 resulting system is capable of handling a large number of scene classes at a fraction of the time required for exhaustively matching sets of keypoints. This supports supports a coarse-to-fine search strategy for approaches reliant on keypoint matching. We test the idea on 3 datasets for particular scene recognition and report the obtained results.