Principles of artificial intelligence
Principles of artificial intelligence
An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
An O(n log n) algorithm for the all-nearest-neighbors problem
Discrete & Computational Geometry
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Augment or push: a computational study of bipartite matching and unit-capacity flow algorithms
Journal of Experimental Algorithmics (JEA)
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
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
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Computer Vision and Image Understanding
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Unifying Framework for Relational Structure Matching
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A POCS-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Protein classification by matching and clustering surface graphs
Pattern Recognition
Graph matching and clustering using spectral partitions
Pattern Recognition
Semantic Description of Aerial Images Using Stochastic Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMD-L1: an efficient and robust algorithm for comparing histogram-based descriptors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A PCA approach for fast retrieval of structural patterns inattributed graphs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A sparse nonnegative matrix factorization technique for graph matching problems
Pattern Recognition
Near-duplicate document image matching: A graphical perspective
Pattern Recognition
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In this paper, we propose a new ARG matching scheme based on the nested assignment structure to assess the similarity between two attributed relational graphs (ARGs). ARGs are represented by nodes and edges containing unary attributes and binary relations between nodes, respectively. The nested assignment structure consists of inner and outer steps. In the inner step, to form a distance matrix, combinatorial differences between every pair of nodes in two ARGs are computed by using an assignment algorithm. Then, in the outer step, a correspondence between nodes in the two ARGs is established by using an assignment algorithm based on the distance matrix. The proposed ARG matching scheme consists of three procedures as follows: first, in the initializing procedure, the nested assignment structure is performed to generate an initial correspondence between nodes in two ARGs. Next, the correspondence is refined by iteratively performing the updating procedure, which also utilizes the nested assignment structure, until the correspondence does not change. Finally, the verifying procedure can be performed in case that some nodes to be matched in the two ARGs are missing. From experimental results, the proposed ARG matching scheme shows superior matching performance and localizes target objects robustly and correctly even in severely noisy and occluded scenes.