FIM: A Real-Time Content Based Sample Image Matching System

Sample Image Matching is to decide if a queried image is belongs to the database or not. In this paper, we focus on real-time image matching, which is critical in many real world applications. Although traditional image retrieval methods can be directly utilized for image matching, they usually suffer the high computational cost problem and thus is not applicable here. To resolve this problem, we first introduce ORB, a recently proposed and well-established image feature, for image matching. Then we compare several variants of the descriptors, different size of the codebook, and two approaches to compute the matching scores, based on which we propose a strategy for final matching decision. According to the comparison results, we finally present a real-time image matching system, fast image matching (FIM), which can process about 33 images per second, with a satisfactory accuracy.