Subgraph Spotting in Graph Representations of Comic Images
Graph are widely used for representing the structure, topology and attributes of underlying information in various application domains of pattern recognition. Information retrieval based on the structural (and topological) similarity between query and retrieval candidates can be best modeled by an attributed graph retrieval problem, which thus is a very important research problem specially for the application domains of structural pattern recognition, computer vision, image analysis, data mining and machine learning. This research problem becomes more challenging if the graphs contain attributes on their nodes and arcs.
The research problem of searching a query graph in a database of graphs is termed as “subgraph spotting”. The proposed competition is focused on the research problem of subgraph spotting in a database of attributed graphs. The goal of the SSGCI competition is to spot a query attributed graph in a database of attributed graphs. This means that for a given query attributed graph the goal is to retrieve every graph in the database which contains this query graph and to provide node correspondences between the query and each of the result graphs.
This main challenge of the SSGCI competition represents an open research problem in graph-based structural pattern recognition. The problems of matching, indexing and retrieval of graph-based representations of underlying data are actively research into by various research teams around the globe. Our target audience are these researchers working on graph matching using exact as well as inexact approaches, who would like to participate in this competition to get their methods benchmarked with respect to the other state of the art methods.