@inproceedings{71f436caa46a404b9e820e8a0ba16c12,
title = "Combining clusterings with different detail levels",
abstract = "In this study we address the problem of recovering a clustering of a dataset based on several clusterings provided by different experts. These experts provide clusterings on different levels (coarser or finer than the others). 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 average segmentation is efficiently computed by first grouping the image into superpixels and then applying the proposed algorithm on the superpixel map. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation dataset.",
keywords = "Ensemble decision, ILP, segmentation",
author = "Oded Kaminsky and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2016",
month = nov,
day = "8",
doi = "https://doi.org/10.1109/mlsp.2016.7738888",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, {Francesco A. N.} and Jan Larsen",
booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",
address = "الولايات المتّحدة",
}