Learning the Sub-optimal Graph Edit Distance Edit Costs Based on an Embedded Model
Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. One of its main constraints is that it requires an adequate definition of edit costs, which eventually determines which graphs are considered similar. These edit costs are usually defined as concrete functions or constants in a manual fashion and little effort has been done to learn them. The present paper proposes a framework to define these edit costs automatically. Moreover, we concretise this framework in two different models based on neural networks and probability density functions.
KeywordsGraph edit distance Edit costs Neural network Probability density function
This research is supported by the Spanish projects TIN2016-77836-C2-1-R and ColRobTransp MINECO DPI2016-78957-R AEI/FEDER EU; and also, the European project AEROARMS, H2020-ICT-2014-1-644271.
- 15.Moreno-García, C.F., Cortés, X., Serratosa, F.: A graph repository for learning error-tolerant graph matching. In: Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F., Wilson, R. (eds.) S+SSPR 2016. LNCS, vol. 10029, pp. 519–529. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49055-7_46CrossRefGoogle Scholar