A generic framework for median graph computation based on a recursive embedding approach

  • Authors:

  • M. Ferrer;D. Karatzas;E. Valveny;I. Bardaji;H. Bunke

  • Affiliations:

  • Institut de Robòtica i Informítica Industrial, CSIC-UPC, C.Llorens Artigas 4-6, 2a planta, 08028 Barcelona, Spain;Centre de Visió per Computador, Departament de Ciències de la Computació, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;Centre de Visió per Computador, Departament de Ciències de la Computació, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;Institut de Robòtica i Informítica Industrial, CSIC-UPC, C.Llorens Artigas 4-6, 2a planta, 08028 Barcelona, Spain;Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

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Abstract

The median graph has been shown to be a good choice to obtain a representative 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 previous 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 median and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four different databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medians, in terms of the sum of distances to the training graphs, than with the previous existing methods.