Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A paraperspective factorization method for shape and motion recovery
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Optimal Point Correspondence through the Use of Rank Constraints
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
The generalized patchmatch correspondence algorithm
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Robust principal component analysis?
Journal of the ACM (JACM)
Foundations and Trends® in Machine Learning
Face recognition in unconstrained videos with matched background similarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Optimal object matching via convexification and composition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We investigate the problem of finding the correspondence from multiple images, which is a challenging combinatorial problem. In this work, we propose a robust solution by exploiting the priors that the rank of the ordered patterns from a set of linearly correlated images should be lower than that of the disordered patterns, and the errors among the reordered patterns are sparse. This problem is equivalent to find a set of optimal partial permutation matrices for the disordered patterns such that the rearranged patterns can be factorized as a sum of a low rank matrix and a sparse error matrix. A scalable algorithm is proposed to approximate the solution by solving two sub-problems sequentially: minimization of the sum of nuclear norm and l1 norm for solving relaxed partial permutation matrices, followed by a binary integer programming to project each relaxed partial permutation matrix to the feasible solution. We verify the efficacy and robustness of the proposed method with extensive experiments with both images and videos.