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Function-described graphs for modelling objects represented by sets of attributed graphs
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DOI：10.1016/S0031-3203(02)00107-3.png)
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Abstract
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We present in this article the model function-described graph (FDG), which is a type of compact representation of a set of attributed graphs (AGs) that borrow from random graphs the capability of probabilistic modelling of structural and attribute information. We define the FDGs, their features and two distance measures between AGs (unclassified patterns) and FDGs (models or classes) and we also explain an efficient matching algorithm. Two applications of FDGs are presented: in the former, FDGs are used for modelling and matching 3D-objects described by multiple views, whereas in the latter, they are used for representing and recognising human faces, described also by several views. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
attributed graphs
error-tolerant graph matching
function-described graphs
random graphs
clustering
synthesis
3D-object recognition and face identification
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