Embedded Double Matching of Local Descriptors for a Fast Automatic Recognition of Real-World Objects

In this paper, we present a new approach for matching local descriptors such as Scale Invariant Feature Transform (SIFT)ones in order to recognize image objects quickly and reliably. The proposed method first computes the Hausdorff distance combined with the City-Block distance to match the two sets of the extracted keypoints from the goal and data images, respectively. Then, the matched points are involved into an embedded pairing process, leading to a double matching which is more discriminant for the object recognition purpose as demonstrated on real-world standard databases.