Abstract
In pattern matching, estimating the spatial distance between generated feature descriptors is known as "similarity matching". It is a fundamental procedure in many application disciplines. The primary axis for rapid and precise similarity matching is the extraction method, which can effectively represent images with low-dimensional descriptors. In this paper, we evaluate the Scale-Invariant Feature Transform (SIFT) due to its relevance and use in numerous applications. However, it has several limitations. Thus, the usage of Oriented FAST and Rotated BRIEF (ORB) is recommended. In the form of three scenarios, this paper introduces similarity matching performance assessment for each method from several perspectives not explicitly expressed in the literature. The first scenario is introduced to assess the extraction of the same features from multiple images. The method evaluation to identify the K most important keypoints is included in the second scenario. The sorting of the used images hinders the feature extraction method as well as the similarity matching. The third scenario involves evaluating the matching performance of visual features extracted with SIFT and ORB from real and sketched images. Furthermore, this paper introduces a step forward to fill the gap between sketch and real domains by converting real images to pencil-sketch images. Finally, the computational complexity of the overall test cases is computed and assessed. According to the results of real-to-pencil-sketch image matching, ORB outperforms SIFT in terms of the computed performance metrics as well as the correct matching by a factor of around two. Besides, SIFT has a temporal complexity that is nearly double that of ORB, which directly affects the capacity of any application relying on similarity matching.