Keypoint Signatures for Fast Learning and Recognition
Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods have recently been proposed to train the classifier online, they can only learn a very limited number of new keypoints. This represents a handicap for real-time applications, such as Simultaneous Localization and Mapping (SLAM), which require incremental addition of arbitrary numbers of keypoints as they become visible. In this paper, we overcome this limitation and propose a descriptor that can be learned online fast enough to handle virtually unlimited numbers of keypoints. It relies on the fact that if we train a Randomized Tree classifier to recognize a number of keypoints extracted from an image database, all other keypoints can be characterized in terms of their response to these classification trees. This signature is fast to compute and has a discriminative power that is comparable to that of the much slower SIFT descriptor.