%0 Conference Proceedings
%T Error-tolerant coarse-to-fine matching model for hierarchical graphs
%A Pau Riba
%A Josep Llados
%A Alicia Fornes
%E Pasquale Foggia
%E Cheng-Lin Liu
%E Mario Vento
%B 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
%D 2017
%V 10310
%I Springer International Publishing
%F Pau Riba2017
%O DAG; 600.097; 601.302; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2951), last updated on Mon, 07 Dec 2020 14:29:44 +0100
%X Graph-based  representations  are  effective  tools  to  capture structural information from visual elements. However, retrieving a query graph  from  a  large  database  of  graphs  implies  a  high  computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social  media  analysis,  we propose  to  generate a  hierarchy  able  to  deal with different levels of abstraction while keeping information about the topology.  For  the  proposed  representations,  a  coarse-to-fine  matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
%K Graph  matching
%K Hierarchical  graph
%K Graph-based  representation
%K Coarse-to-fine matching
%U http://refbase.cvc.uab.es/files/RLF2017.pdf
%U http://dx.doi.org/10.1007/978-3-319-58961-9
%P 107-117