Optimal solutions for semantic image decomposition

Bridging the gap between low-level and high-level image analysis has been a central challenge in computer vision throughout the last decades. In this article I will point out a number of recent developments in low-level image analysis which open up new possibilities to bring together concepts of high-level and low-level vision. The key observation is that numerous multi-label optimization problems can nowadays be efficiently solved in a near-optimal manner, using either graph-theoretic algorithms or convex relaxation techniques. Moreover, higher-level semantic knowledge can be learned and imposed on the basis of such multi-label formulations.