A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
A Unified Framework for Indexing and Matching Hierarchical Shape Structures
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of three maximum common subgraph algorithms on a large database of labeled graphs
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Complex Fiedler Vectors for Shape Retrieval
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
From geometric to semantic human body models
Computers and Graphics
Comparing sets of 3D digital shapes through topological structures
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Reeb graph based partial shape retrieval for non-rigid 3D object
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
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This paper presents an algorithm for the computation of the maximum common subgraph (MCS) between two directed, acyclic graphs with attributes. The core of the contribution resides in the modularity of the proposed algorithm which allows different heuristic techniques to be plugged in, depending on the application domain. Implemented heuristics for robust graph matching with respect to graph structural noise are discussed. As example of its effectiveness, the algorithm is applied to the problem of 3D shape similarity evaluation through structural shape descriptors.