For the segmentation of moving objects in videos, the analysis of long-term point trajectories has been very popu-lar recently. In this paper, we formulate the segmentation of a video sequence based on point trajectories as a minimum cost multicut problem. Unlike the commonly used spectral clustering formulation, the minimum cost multicut formu-lation gives natural rise to optimize not only for a clus-ter assignment but also for the number of clusters while allowing for varying cluster sizes. In this setup, we pro-vide a method to create a long-term point trajectory graph with attractive and repulsive binary terms and outperform state-of-the-art methods based on spectral clustering on the FBMS-59 dataset and on the motion subtask of the VSB100 dataset. 1.