On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
A Riemannian approach to graph embedding
Pattern Recognition
Topological model for 3D image representation: Definition and incremental extraction algorithm
Computer Vision and Image Understanding
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
A Polynomial Algorithm for Submap Isomorphism
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Efficient Encoding of n-D Combinatorial Pyramids
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Polynomial algorithms for subisomorphism of nD open combinatorial maps
Computer Vision and Image Understanding
Measuring the distance of generalized maps
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Speeding up graph edit distance computation through fast bipartite matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Hierarchical interactive image segmentation using irregular pyramids
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
| Hi-index | 0.10 |
Generalized maps are widely used to model the topology of nD objects (such as images) by means of incidence and adjacency relationships between cells (vertices, edges, faces, volumes, etc.). In this paper, we introduce distance measures for comparing generalized maps, which is an important issue for image processing and analysis. We introduce a first distance measure which is defined by means of the size of a largest common submap. This distance is generic: it is parameterized by a submap relation (which may either be induced or partial), and by weights to balance the importance of darts with respect to seams. We show that this distance measure is a metric. We also introduce a map edit distance, which is defined by means of a minimum cost sequence of edit operations that should be performed to transform a map into another map. We relate maximum common submaps with the map edit distance by introducing special edit cost functions for which they are equivalent. We experimentally evaluate these distance measures and show that they may be used to classify meshes.