Efficient Pose and Cell Segmentation using Column Generation
  We study the problems of multi-person pose segmentation in natural images and instance segmentation in biological images with crowded cells. We formulate these distinct tasks as integer programs where variables correspond to poses/cells. To optimize, we propose a generic relaxation scheme for solving these combinatorial problems using a column generation formulation where the program for generating a column is solved via exact optimization of very small scale integer programs. This results in efficient exploration of the spaces of poses and cells. 
Shaofei Wang, Chong Zhang, Miguel A. Gonzalez-Ballester, Julian   Yarkony
cs.CV