%0 Journal Article
%T Graph Matching: a New Concave Relaxation Function and Algorithm<br>图模型匹配:一种新的凹松弛函数及算法
%A LIU Zhi-Yong
%A <br>刘智勇
%J 自动化学报
%D 2012
%I 
%X Recently, approximate graph matching based on relaxing the permutation matrix to a doubly stochastic matrix has become an important and popular topic. The key point lies in which approximation over a continuous set is usually easier to implement than that over a discrete one. However, a consequent trouble related to such a relaxation is how to properly map the doubly stochastic matrix back to a permutation one. In the literature, a concave relaxation function for matching problem between the undirected graphs without self-loops was recently proposed, such that the doubly stochastic matrix can converge to a permutation one in a smooth way, and got a state-of-art performance on matching accuracy. Unfortunately, except for the undirected graphs without self-loops, there are no concave relaxation proposed for any other types of graph models. In this paper, we propose a concave relaxation for the directed graphs without self-loops, based on which a graph matching algorithm is then presented. Extensive experimental comparisons witness the validity of the proposed methods.
%K Graph matching
%K Frank-Wolfe algorithm
%K concave relaxation
%K convex relaxation<br>图模型匹配
%K Frank-Wolfe算法
%K 凹松弛函数
%K 凸松弛函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=1393146D229514ED6A34BD2E4584A655&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=00B9006659EBD8AC&eid=507521DBC725630F&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=17