SConE: Siamese Constellation Embedding Descriptor for Image Matching
This addresses the issue of filtering out inconsistent matches in computer vision applications, though it is an incremental improvement over existing descriptors.
The paper tackled the problem of geometrically inconsistent keypoint matches in image matching by proposing a novel descriptor that incorporates both local features and geometric layout of neighboring keypoints, achieving significant performance improvement on the TUM dataset.
Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching. Although their local character makes image matching processes more robust to occlusions, it often leads to geometrically inconsistent keypoint matches that need to be filtered out, e.g. using RANSAC. In this paper we propose a novel, more discriminative, descriptor that includes not only local feature representation, but also information about the geometric layout of neighbouring keypoints. To that end, we use a Siamese architecture that learns a low-dimensional feature embedding of keypoint constellation by maximizing the distances between non-corresponding pairs of matched image patches, while minimizing it for correct matches. The 48-dimensional oating point descriptor that we train is built on top of the state-of-the-art FREAK descriptor achieves significant performance improvement over the competitors on a challenging TUM dataset.