Metric details for natural-language spatial relations
ACM Transactions on Information Systems (TOIS)
Shock Graphs and Shape Matching
International Journal of Computer Vision
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Structure-Based Similarity Search with Graph Histograms
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
A fast technique for comparing graph representations with applications to performance evaluation
International Journal on Document Analysis and Recognition
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
An online composite graphics recognition approach based on matching of spatial relation graphs
International Journal on Document Analysis and Recognition
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
NAVIDOMASS: Structural-based Approaches Towards Handling Historical Documents
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Using Spatial Relations for Graphical Symbol Description
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Hypergraph-based image retrieval for graph-based representation
Pattern Recognition
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Graphs give a universal and flexible framework to describe the structure and relationship between objects. They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.