A Method for Image Feature Points Selection

Image points are important local features for image description and matching. However, popular point detectors usually extract thousands of points, which causes over-description and increases computational complexity for subsequent processing. To select a set of useful points to describe images, most methods simply choose points according to its response function. But these methods mostly overlook the distribution uniformity and distinctiveness of the points. In this paper, a novel method for selecting a set of points based on pruning of the MST is proposed. Firstly, a MST is constructed using the points extracted by some certain point detector. Then the MST is pruned to select a set of points which are optimal or suboptimal on stability, uniformity and distinctiveness in describing the structure of the image. Experimental results demonstrate that the proposed method outperforms traditional point selection methods.