Learning to Associate Image Features with CRF-Matching

<p>This paper presents a supervised learning algorithm for image feature matching. The algorithm is based on Conditional Random Fields which provides a mechanism for globally reason about the associations. The novelty of this work is twofold: (i) the use of Delaunay triangulation as the graph structure for a probabilis- tic network to reason about image feature association; (ii) the combination of local and joint features to enforce consistency in a theoretically sound statistical learning procedure. Experimental results show that our approach outperforms RANSAC in our challenging datasets consisting of indoor and outdoor images, with significant occlusion, blurring, rotational and translational transformations.</p>

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