Abstract Multi-Camera Multi-Object Tracking is currently drawing attention in the computer
vision field due to its superior performance in real-world applications such as video …

Image partitioning, or segmentation without semantics, is the task of decomposing an image
into distinct segments; or equivalently, the task of detecting closed contours in an image …

Constrained clustering extends clustering by integrating user constraints, and aims to
determine an optimal assignment under the constraints. In this paper, we propose a local …

Correlation clustering is a widely studied framework for clustering based on pairwise
similarity and dissimilarity scores, but its best approximation algorithms rely on impractical …

The minimum cost lifted multicut problem is a generalization of the multicut problem (also
known as correlation clustering) and is a means to optimizing a decomposition of a graph …

Neuron reconstruction algorithms used in electron microscope volumes have received
increasing attention in recent years. Most current methods are highly reliant on neuron …

We propose a highly parallel primal-dual algorithm for the multicut (aka correlation
clustering) problem, a classical graph clustering problem widely used in machine learning …

Calcium imaging is one of the most important tools in neurophysiology as it enables the
observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution …

Weighted correlation clustering is hard to solve and hard to approximate for general graphs.
Its applications in network analysis and computer vision call for efficient algorithms. To this …

We propose a theoretical framework that generalizes simple and fast algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive …