2017年09月24日 |  English version
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A Quadratic Penalty Method for Hypergraph Matching

报告题目:A Quadratic Penalty Method for Hypergraph Matching

报告人:崔春风,香港城市大学

报告时间:2017年8月18日(周五)10:00

报告地点:行健楼学术报告室526

邀请人:姜波博士

Abstract:Hypergraph matching is a fundamental problem in computer vision. Mathematically, it maximizes a polynomial objective function, subject to assignment constraints. In this paper, we reformulate the hypergraph matching problem as a sparse constrained optimization problem. By dropping the sparse constraint, we show that the resulting relaxation problem can recover the global minimizer of the original problem. This heavily depends on the special structures of the hypergraph matching. The critical step in solving the original problem is to identify the location of nonzero entries (referred to as support set) in a global minimizer. Inspired by such observation, we apply the quadratic penalty method to solve the relaxation problem. Under reasonable assumptions, we show that the support set of the global minimizer in a hypergraph matching problem can be correctly identified when the number of iterations is sufficiently large. A projected gradient method is applied as a subsolver to solve the quadratic penalty subproblem. Numerical results demonstrate that the exact recovery of the support set indeed happens, and the proposed algorithm is efficient in terms of both accuracy and CPU time.  This is a joint work with Prof. Qingna Li, Prof. Liqun Qi and Prof. Hong Yan.

CV: Chunfeng Cui received the B.S. degree in mathematics from Jilin University, ChangChun, China, in 2011, and the Ph.D degree in computational mathematicsfrom Academy of Mathematics and Systems Science, Chinese Academy ofSciences, Beijing, China, in 2016. Currently, she is a postdoctoral researcher at department of electronic engineering, City University of Hong Kong. Her research interests include  large scale tensor computations. E-mail:chunfengcui89@gmail.com

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