Finesse Based Ensemble Segmentation
Abstract: We address the problem of recovering a clustering of a dataset based on several clusterings provided by different experts. We present an automatic algorithm that combines the information provided by the experts into a single clustering that can be viewed as the average point of the input clusterings. We formulate the problem as an instance of correlation clustering and apply integer linear programming to obtain the average clustering. As a byproduct, we also obtain for each expert its reliability and the detail level encoded in its clustering. We apply the proposed algorithm to the task of averaging several image segmentations.
The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation dataset.
* This research is an M.Sc. thesis under the supervision of Jacob Goldberger.