Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Model matching in robot vision by subgraph isomorphism
Pattern Recognition
Embedding tree metrics into low dimensional Euclidean spaces
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)
SIAM Journal on Computing
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic-based search for error-correcting graph isomorphism
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient many-to-many feature matching under the l1 norm
Computer Vision and Image Understanding
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The problem of object recognition can be formulated as matching feature sets of different objects. Segmentation errors and scale difference result in many-to-many matching of feature sets, rather than one-to-one. This paper extends a previous algorithm on many-to-many graph matching. The proposed work represents graphs, which correspond to objects, isometrically in the geometric space under the l 1 norm. Empirical evaluation of the algorithm on a set of recognition trails, including a comparison with the previous approach, demonstrates the efficacy of the overall framework.