Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets

Fuchs, Mathias; Riesen, Kaspar (2022). Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 25th Iberoamerican Congress, CIARP 2021. Lecture Notes in Computer Science: Vol. 12702 (pp. 382-391). Springer 10.1007/978-3-030-93420-0_36

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Both the amount of data available and the rate at which it is acquired increases rapidly. The underlying data is often complex, making it difficult (or somehow unnatural) to represent it by vectorial data structures. Hence, graphs are a promising alternative for formalizing the data. Actually a large amount of graph-based methods for pattern recognition have been proposed. The vast amount of these methods rely on graph matching procedures. In a recent paper a novel encoding of graph matching information has been proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs (called matching-graphs). In the present paper we aim to further improve the relevance of these matching-graphs by using an iterative creation algorithm. In an empirical evaluation we show that these novel matching-graphs offer a more stable and significant representation of their respective class than the previous version.

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