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Author (up) Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; I. Bardaji; Horst Bunke edit  openurl
  Title A Generic Framework for Median Graph Computation based on a Recursive Embedding Approach Type Journal Article
  Year 2011 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 115 Issue 7 Pages 919-928  
  Keywords Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition  
  Abstract The median graph has been shown to be a good choice to obtain a represen- tative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previ- ous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set me- dian and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four dif- ferent databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medi- ans, in terms of the sum of distances to the training graphs, than with the previous existing methods.  
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  Publisher Place of Publication Editor  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number IAM @ iam @ FKV2011 Serial 1831  
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