Pattern Recognition, International Conference on (2006)
Aug. 20, 2006 to Aug. 24, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.947
HongFang Wang , University of York, York, UK
Edwin R. Hancock , University of York, York, UK
In this paper a new formulation of probabilistic relaxation labeling is developed using the theory of diffusion processes on graphs. According to this picture, the label probabilities are given by the state-vector of a continuous time random walk on a support graph. The state-vector is the solution of the heat equation on the support-graph. The nodes of the support graph are the Cartesian product of the object-set and label-set of the relaxation process. The compatibility functions are combined in the weight matrix of the support graph. The solution of the heat-equation is found by exponentiating the eigensystem of the Laplacian matrix for the weighted support graph with time. We demonstrate the new relaxation process on a feature correspondence matching problem abstracted in terms of relational graphs.
H. Wang and E. R. Hancock, "Probabilistic Relaxation using the Heat Equation," 2006 18th International Conference on Pattern Recognition(ICPR), Hong Kong, 2006, pp. 666-669.