Graph matching - Challenges and potential solutions

Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.