An Experimental Comparison of Trust Region and Level Sets
This work provides an incremental comparison of two existing optimization methods for computer vision researchers, helping them choose the right approach for non-linear functional problems.
The paper compared the practical efficiency, robustness, and optimality of trust region and level sets frameworks for optimizing high-order functionals in computer vision problems like segmentation and stereo, finding that trust region methods often performed better in terms of speed and parameter sensitivity.
High-order (non-linear) functionals have become very popular in segmentation, stereo and other computer vision problems. Level sets is a well established general gradient descent framework, which is directly applicable to optimization of such functionals and widely used in practice. Recently, another general optimization approach based on trust region methodology was proposed for regional non-linear functionals. Our goal is a comprehensive experimental comparison of these two frameworks in regard to practical efficiency, robustness to parameters, and optimality. We experiment on a wide range of problems with non-linear constraints on segment volume, appearance and shape.