Theory of linear and integer programming
Theory of linear and integer programming
An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Convergence properties of the softassign quadratic assignment algorithm
Neural Computation
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical models for graph matching: Approximate models and optimal algorithms
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Combinatorial Optimization: Theory and Algorithms
Combinatorial Optimization: Theory and Algorithms
Many-to-many matching of scale-space feature hierarchies using metric embedding
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Improved spectral relaxation methods for binary quadratic optimization problems
Computer Vision and Image Understanding
A Path Following Algorithm for Graph Matching
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Semidefinite Programming Heuristics for Surface Reconstruction Ambiguities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Correspondence propagation with weak priors
IEEE Transactions on Image Processing
A bound for non-subgraph isomorphism
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Improving shape retrieval by spectral matching and meta similarity
IEEE Transactions on Image Processing
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
Recognition of occluded objects by reducing feature interactions
Image and Vision Computing
Message-Passing Algorithms for Sparse Network Alignment
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.