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Feature Tracking Utilizing a Maximum-Weight Independent Set Problem


Andrea Schnorr, Dirk Norbert Helmrich, Hank Childs, Torsten Wolfgang Kuhlen, Bernd Hentschel
The 9th IEEE Symposium on Large Data Analysis and Visualization (LDAV 2019)
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Tracking the temporal evolution of features in time-varying data remains a combinatorially challenging problem. A recent method models event detection as a maximum-weight independent set problem on a graph representation of all possible explanations [35]. However, optimally solving this problem is NP-hard in the general case. Following the approach by Schnorr et al., we propose a new algorithm for event detection. Our algorithm exploits the modelspecific structure of the independent set problem. Specifically, we show how to traverse potential explanations in such a way that a greedy assignment provides reliably good results. We demonstrate the effectiveness of our approach on synthetic and simulation data sets, the former of which include ground-truth tracking information which enable a quantitative evaluation. Our results are within 1% of the theoretical optimum and comparable to an approximate solution provided by a state-of-the-art optimization package. At the same time, our algorithm is significantly faster.

» Show BibTeX

@InProceedings{Schnorr2019,
author = {Andrea Schnorr, Dirk Norbert Helmrich, Hank Childs, Torsten Wolfgang Kuhlen, Bernd Hentschel},
title = {{Feature Tracking Utilizing a Maximum-Weight Independent Set Problem}},
booktitle = {9th IEEE Symposium on Large Data Analysis and Visualization},
year = {2019}
}




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