Matching Features Correctly through Semantic Understanding
Image-to-image feature matching is the single most restrictive time bottleneck in any matching pipeline. We propose two methods for improving the speed and quality by employing semantic scene segmentation. First, we introduce a way of capturing semantic scene context of a key point into a compact description. Second, we propose to learn correct match ability of descriptors from these semantic contexts. Finally, we further reduce the complexity of matching to only a pre-computed set of semantically close key points. All methods can be used independently and in the evaluation we show combinations for maximum speed benefits. Overall, our proposed methods outperform all baselines and provide significant improvements in accuracy and an order of magnitude faster key point matching.