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Second-order random graphs for modelling sets of attributed graphs and their application to object learning and recognition

AuthorsSanfeliu, Alberto ; Serratosa, Francesc; Alquézar Mancho, Renato
KeywordsRandom graphs
Graph synthesis
Distance measure
Object learning
Object recognition
Pattern recognition systems
Issue Date2004
PublisherWorld Scientific Publishing
CitationInternational Journal of Pattern Recognition and Artificial Intelligence 18(3): 375-396 (2004)
AbstractThe aim of this article is to present a random graph representation, that is based on second-order relations between graph elements, for modeling sets of attributed graphs (AGs). We refer to these models as Second-Order Random Graphs (SORGs). The basic feature of SORGs is that they include both marginal probability functions of graph elements and second-order joint probability functions. This allows a more precise description of both the structural and semantic information contents in a set of AGs and, consequently, an expected improvement in graph matching and object recognition. The article presents a probabilistic formulation of SORGs that includes as particular cases the two previously proposed approaches based on random graphs, namely the First-Order Random Graphs (FORGs) and the Function-Described Graphs (FDGs). We then propose a distance measure derived from the probability of instantiating a SORG into an AG and an incremental procedure to synthesize SORGs from sequences of AGs. Finally, SORGs are shown to improve the performance of FORGs, FDGs and direct AG-to-AG matching in three experimental recognition tasks: one in which AGs are randomly generated and the other two in which AGs represent multiple views of 3D objects (either synthetic or real) that have been extracted from color images. In the last case, object learning is achieved through the synthesis of SORG models.
Publisher version (URL)http://dx.doi.org/10.1142/S0218001404003253
Appears in Collections:(IRII) Artículos
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