Generalized Median Graph via Iterative Alternate Minimizations
Computer Vision and Pattern Recognition
2019-06-27 v1 Quantitative Methods
Abstract
Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.
Cite
@article{arxiv.1906.11009,
title = {Generalized Median Graph via Iterative Alternate Minimizations},
author = {Nicolas Boria and S'ebastien Bougleux and Benoit Gaüzère and Luc Brun},
journal= {arXiv preprint arXiv:1906.11009},
year = {2019}
}