A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generative Image Segmentation Using Random Walks with Restart
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Balanced feature matching in probabilistic framework and its application on object localisation
International Journal of Computer Applications in Technology
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In this paper, we propose an efficient method for finding consistent correspondences between two sets of features Our matching algorithm augments the discriminative power of each correspondence with the spatial consistency directly estimated from a graph that captures the interactions of all correspondences by using Random Walks with Restart (RWR), one of the well-established graph mining techniques The $\it{steady}$-$\it{state}$ probabilities of RWR provide the global relationship between two correspondences by the local affinity propagation Since the correct correspondences are likely to establish global interactions among them and thus form a strongly consistent group, our algorithm efficiently produces the confidence of each correspondence as the likelihood of correct matching We recover correct matches by imposing a sequential method with mapping constraints in a simple way The experimental evaluations show that our method is qualitatively and quantitatively robust to outliers, and accurate in terms of matching rate in various matching frameworks.