Fast Rotation-Invariant DAISY Descriptor for Image Keypoint Matching

In this paper, we introduce an improved version of DAISY descriptor algorithm for fast and high-quality image key point matching. Since DAISY descriptor algorithm has many prominent advantages but lacks the ability of handling large in-plane rotation, we thus construct our Rotation-Invariant DAISY descriptor to effectively settle this shortcoming. We first extract key points of images by Harris corner detector, and then use histogram data of every key point to assign a local orientation for each. During the step of constructing descriptors, we rotate the coordinate axis of every key point to its local orientation, and obtain Rotation-Invariant DAISY descriptors. In order to accelerate matching task, we apply PCA to descriptors for dimension reduction. Our experiments demonstrate that the Fast Rotation-Invariant DAISY has better performance in various image distortions. The results also testify that this approach consumes less time when obtains similar results of SIFT descriptor algorithm, and it can be applied more densely in different kinds of images but SIFT cannot.