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Learning graph prototypes for shape recognition

Abstract : This paper presents some new approaches for computing graph prototypes in the context of the design of a structural nearest prototype classifier. Four kinds of prototypes are investigated and compared : set median graphs, generalized median graphs, set discriminative graphs and generalized discriminative graphs. They differ according to (i) the graph space where they are searched for and (ii) the objective function which is used for their computation. The first criterion allows to distinguish set prototypes which are selected in the initial graph training set from generalized prototypes which are generated in an infinite set of graphs. The second criterion allows to distinguish median graphs which minimize the sum of distances to all input graphs of a given class from discriminative graphs, which are computed using classification performance as criterion, taking into account the inter-class distribution. For each kind of prototype, the proposed approach allows to identify one or many prototypes per class, in order to manage the trade-off between the classification accuracy and the classification time.
Keywords : Symbol recognition
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Contributor : Sébastien Adam <>
Submitted on : Monday, May 16, 2011 - 9:12:51 AM
Last modification on : Wednesday, October 14, 2020 - 3:55:16 AM
Long-term archiving on: : Friday, November 9, 2012 - 11:30:39 AM


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Romain Raveaux, Sébastien Adam, Pierre Héroux, Éric Trupin. Learning graph prototypes for shape recognition. Computer Vision and Image Understanding, Elsevier, 2011, 115 (7), pp.905 - 918. ⟨10.1016/j.cviu.2010.12.015⟩. ⟨hal-00593453⟩



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