A Graph Repository for Learning Error-Tolerant Graph Matching
In the last years, efforts in the pattern recognition field have been especially focused on developing systems that use graph based representations. To that aim, some graph repositories have been presented to test graph-matching algorithms or to learn som
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ching algorithm
Graph-learning
1 Introduction In pattern recognition, benchmarking is the process of measuring the quality of the representation of the objects, or the quality of the algorithms involved on comparing, classifying or clustering these objects. The objective of benchmarking is to improve performance of the involved object representations and pattern recognition algorithms. Pattern recognition, through graph-based representations, has been developed through the last forty years with great success and acknowledgement. Interesting surveys about this subject are [1, 2] or [3]. The first error-tolerant graph matching algorithms were published in 1983, [4, 5], and since then, several new algorithms have been presented.
This research is supported by projects DPI2013-42458-P TIN2013-47245-C2-2-R and by Consejo Nacional de Ciencia y Tecnología (CONACyT México). © Springer International Publishing AG 2016 A. Robles-Kelly et al. (Eds.): S+SSPR 2016, LNCS 10029, pp. 519–529, 2016. DOI: 10.1007/978-3-319-49055-7_46
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For this reason, in 2008, a specific database to perform benchmarking on graph databases was published for the first time [6]. As authors reported, they presented such database and published its paper with the aim of providing to the scientific community a public and general framework to evaluate graph representations and graph algorithms [7–9], such as error-tolerant graph matching, [10–15] learning the consensus of several correspondences, [16–20], image registration based on graphs, [21, 22], learning graph-matching parameters [23, 24], and so on. Note that a huge amount of methods has been presented, and the previous list is simply a small sample of them. For a detailed list of methods, we refer to the aforementioned surveys [1–3]. This database, called IAM [25], has been largely cited and used to develop new algorithms. It is composed of twelve datasets containing diverse attributed graphs, for instance, proteins, fingerprints, hand written characters, among others. With the same idea, another graph database had been previously published in 2001 [26, 27]. Nevertheless, the aim of this database [28] is to perform exact isomorphism benchmarking and cannot be used to test error-tolerant graph matching since nodes and edges are unattributed. It contains 166’000 graphs with very diverse graph sizes. Most recently in 2015 [29], a new graph repository [30] was presented in order to compare exact graph edit distance (GED) calculation methods, where data from [26, 31] was collected and enhanced using low-level information. Note that other papers have presented with new graph-based methodologies and, with the aim of experimental reproducibility, reported their self-made databases and made them public. This is the case of the one first presented in 2006 [32, 33]. It is composed of attributed graphs extracted from image sequences taken from the CMU repository [34]. Graph nodes represent salient points of some images and graph edges have been generated through Delaunay triangulati
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