Feature descriptor matching plays an important role in many computer vision applications. This paper presents a novel fast linear exhaustive search algorithm combined with a multi-resolution candidate elimination technique to deal with this problem efficiently. The proposed algorithm is inspired from the existing multi-resolution image retrieval approaches, but releasing the requirement on a norm-sorted database with pre-computed multi-resolution tables. This helps to increase the applicability of the proposed method. Moreover, the computations of candidate elimination are fully performed using a simple L1 distance metric, which is able to speedup the entire search process without loss of accuracy. This property leads to an accurate feature descriptor matching algorithm with real-time performance, which will be validated in the experiments by testing with the matching of SURF descriptors.
