On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
SIAM Journal on Matrix Analysis and Applications
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Cue-Based visual object contour tracking with incremental learning
Transactions on Edutainment IX
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This paper addresses the problem of finding correspondences between two sets of features by using multiple order constraints all together. First, we build a high-order supersymmetric tensor, called multiple order tensor, to incorporate the constraints of different orders (e.g., unary, pairwise, third order, etc.). The multiple order tensor naturally merges multi-granularity geometric affinities, thus it presents stronger descriptive power of consistent constraints than the individual order based methods. Second, to achieve the optimal matching, we present an efficient computational approach for the power iteration of the multiple order tensor. It only needs sparse tensor elements and reduces the sampling size of feature tuples, due to the supersymmetry of the multiple order tensor. The experiments on both synthetic and real image data show that our approach improves the matching performance compared to state-of-the-art algorithms.