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题目：An Integer Linear Programming Formulation for the Multi-label MRF with Connectivity Constraints

主讲人：Ruobing Shen（沈若冰）,欧盟玛丽居里学者，德国海德堡大学组合与优化实验室研究员

时间：5月16日，10:00-12:00

地点：新主楼A1028

邀请人:张人千教授

Abstract:

Markov random field (MRF) is a set of random variables having a Markov property described by an undirected graph. Integer Linear Programming (ILP) formulations of MRF models with global connectivity priors were investigated previously in computer vision. In these works, only Linear Programming (LP) relaxations or simplified versions of the problem were solved. This paper investigates the ILP of multi-label MRF with exact connectivity priors via a branch-and-cut method, which provably finds globally optimal solutions. The method enforces connectivity priors iteratively by a cutting plane method and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier. The proposed ILP can be applied as a post-processing method on top of any existing approach. As it provides a globally optimal solution, it can be used off-line to generate ground-truth labeling, which serves as a quality check for any fast algorithm. Furthermore, it can be combined with Convolutional Neural Networks (CNN) to generate ground-truth for weakly supervised semantic segmentation. As the underlying model is based on a graph, further applications in operations research include, but are not limited to, the forest harvest planning and territory design problem.

Bio:

Ruobing Shen obtained his master's degree in Operations Research (OR) from Clemson University, USA. He was a European Marie Curie researcher in the Mixed Integer Nonlinear Optimization (MINO) project, and is now a scientific research staff in the Discrete and Combinatorial Optimization group at Heidelberg University. During his Ph.D. stage, he was a research intern at IBM Cplex, Italy and a visiting scholar at Bologna University Italy and Ecole Polytechnique France for a total of 10 months.

His research interests include optimization theory, mixed integer programming and their applications in supply chain management (SCM), machine learning, and computer vision.

With the mission to promote OR and its applications in SCM and artificial intelligence in China, he founded and is now the chief editor of the Zhihu column and WeChat Official Account “运筹OR帷幄”. He also initiated an online Operations Research community with more than 3000 OR related talents.

Ruobing Shen（沈若冰）系美国克莱姆森大学运筹学硕士，欧盟玛丽居里MINO项目学者，德国海德堡大学组合优化博士，现任海德堡大学交叉学科计算中心、离散与组合优化实验室研究员。博士期间曾前往意大利IBM Cplex实习半年，法国巴黎综合理工学术访问一季。主要研究方向为最优化理论、整数规划、供应链管理、机器学习和计算机视觉。创办知乎专栏|微信公众号『运筹OR帷幄』，以及全球运筹学|人工智能高端人才社区，旨在普及运筹学及其在供应链管理|人工智能的应用。

经管学院科研办

2018-05-03