TY  - CONF
AU  - Pau Riba
AU  - Josep Llados
AU  - Alicia Fornes
A2  - GbRPR
ED  - Pasquale Foggia
ED  - Cheng-Lin Liu
ED  - Mario Vento
PY  - 2017//
TI  - Error-tolerant coarse-to-fine matching model for hierarchical graphs
BT  - 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
SP  - 107
EP  - 117
VL  - 10310
PB  - Springer International Publishing
KW  - Graph  matching
KW  - Hierarchical  graph
KW  - Graph-based  representation
KW  - Coarse-to-fine matching
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/RLF2017.pdf
UR  - http://dx.doi.org/10.1007/978-3-319-58961-9
N1  - DAG; 600.097; 601.302; 600.121
ID  - Pau Riba2017
ER  -