Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
This work addresses a combinatorial optimization problem that unifies multiple computer vision tasks, offering a novel abstraction but with incremental algorithmic contributions.
The authors tackled the problem of jointly decomposing a graph and labeling its nodes, which generalizes NP-hard problems like the unconstrained integer quadratic program and minimum cost lifted multicut, and applied local search algorithms to achieve state-of-the-art accuracy in computer vision tasks such as instance-separating semantic segmentation, articulated human body pose estimation, and multiple object tracking.
We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.