Graph algorithms and NP-completeness
Graph algorithms and NP-completeness
A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
A Fast Backtracking Algorithm to Test Directed Graphs for Isomorphism Using Distance Matrices
Journal of the ACM (JACM)
On graphs with unique node labels
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Measuring the similarity of labeled graphs
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm
Graphics Recognition. Recent Advances and New Opportunities
A Graph Based Data Model for Graphics Interpretation
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Supporting the design process with hypergraph genetic operators
Advanced Engineering Informatics
| Hi-index | 0.00 |
In this paper, we propose to represent shapes by graphs. Based on graphic primitives extracted from the binary images, attributed relational graphs were generated. Thus, the nodes of the graph represent shape primitives like vectors and quadrilaterals while arcs describing the mutual primitives relations. To be invariant to transformations such as rotation and scaling, relative geometric features extracted from primitives are associated to nodes and edges as attributes. Concerning graph matching, due to the fact of NP-completeness of graph-subgraph isomorphism, a considerable attention is given to different strategies of inexact graph matching. We also present a new scoring function to compute a similarity score between two graphs, using the numerical values associated to the nodes and edges of the graphs. The adaptation of a greedy graph matching algorithm with the new scoring function demonstrates significant performance improvements over traditional exhaustive searches of graph matching.