Progressive feature matching via triplet graph

Feature based image matching is essential for many computer vision applications. Recently, progressive methods which iteratively enrich the candidate matches and reject the wrong ones have attracted a lot of attentions due to its high precision/recall and efficiency. Its quality of enrichment and rejection relies heavily on the accuracy of the estimated local affine transformation and the capability of the geometric constraint constructed between features. In this paper, we propose a novel progressive feature matching algorithms based on triplet graph, which will produce a more general local affine transformation estimation method, and results in a powerful affine invariant constraint and efficient MRF optimization in rejecting mismatches. Experimental results on several challenging datasets have illustrated our method can achieve much higher precision/recall than existing methods.